{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gG-V_KZzqSSr"
   },
   "source": [
    "#  Conditional Generative Adversarial Network\n",
    "\n",
    "A Generative Adversarial Network (GAN) is a type of generative model.  It consists of two parts called the \"generator\" and the \"discriminator\".  The generator takes random values as input and transforms them into an output that (hopefully) resembles the training data.  The discriminator takes a set of samples as input and tries to distinguish the real training samples from the ones created by the generator.  Both of them are trained together.  The discriminator tries to get better and better at telling real from false data, while the generator tries to get better and better at fooling the discriminator.\n",
    "\n",
    "A Conditional GAN (CGAN) allows additional inputs to the generator and discriminator that their output is conditioned on.  For example, this might be a class label, and the GAN tries to learn how the data distribution varies between classes.\n",
    "\n",
    "## Colab\n",
    "\n",
    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/Conditional_Generative_Adversarial_Networks.ipynb)\n",
    "\n",
    "## Setup\n",
    "\n",
    "To run DeepChem within Colab, you'll need to run the following cell of installation commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 188
    },
    "colab_type": "code",
    "id": "xDBRoR3pFeGs",
    "outputId": "d336d18f-703d-4268-c5eb-e39d6ce86148"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.8.1.dev'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install --pre deepchem\n",
    "import deepchem\n",
    "deepchem.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vr4T07_aqSS_"
   },
   "source": [
    "For this example, we will create a data distribution consisting of a set of ellipses in 2D, each with a random position, shape, and orientation.  Each class corresponds to a different ellipse.  Let's randomly generate the ellipses.  For each one we select a random center position, X and Y size, and rotation angle.  We then create a transformation matrix that maps the unit circle to the ellipse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IdfLLsjGqSTC"
   },
   "outputs": [],
   "source": [
    "import deepchem as dc\n",
    "import numpy as np\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "n_classes = 4\n",
    "batch_size = 100\n",
    "class_centers = np.random.uniform(-4, 4, (n_classes, 2))\n",
    "class_transforms = []\n",
    "for i in range(n_classes):\n",
    "    xscale = np.random.uniform(0.5, 2)\n",
    "    yscale = np.random.uniform(0.5, 2)\n",
    "    angle = np.random.uniform(0, np.pi)\n",
    "    m = [[xscale*np.cos(angle), -yscale*np.sin(angle)],\n",
    "         [xscale*np.sin(angle), yscale*np.cos(angle)]]\n",
    "    class_transforms.append(m)\n",
    "class_transforms = np.array(class_transforms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xPml_fFGqSTK"
   },
   "source": [
    "This function generates random data from the distribution.  For each point it chooses a random class, then a random position in that class' ellipse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ksP0E2KHqSTM"
   },
   "outputs": [],
   "source": [
    "def generate_data(n_points):\n",
    "    classes = np.random.randint(n_classes, size=n_points)\n",
    "    r = np.random.random(n_points)\n",
    "    angle = 2*np.pi*np.random.random(n_points)\n",
    "    points = (r*np.array([np.cos(angle), np.sin(angle)])).T\n",
    "    points = np.einsum('ijk,ik->ij', class_transforms[classes], points)\n",
    "    points += class_centers[classes]\n",
    "    return classes, points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yvf85D4KqSTW"
   },
   "source": [
    "Let's plot a bunch of random points drawn from this distribution to see what it looks like.  Points are colored based on their class label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "CXy5-cJkqSTk",
    "outputId": "afb38088-aa6f-4414-98b2-285b473b140c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe7febc0ee0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dsanGkVHfzq24/fSxUYmQlXt8XPfyZOat3BxxH7AMpWE9OnD24QMYfGD7qPpGSjT7tzJEFAqFQrHXKS338MP8lcxfuRkJHNSlDUs37OCXf1bXS0a9Y4s07j57HBc88VnIdrpmiaItD1GsTtcEh/TqyLOXT6pxfXtOIUvWb0cg6NGhBWc9/CHF5cFryARC0wROm413bz6Nrm2aRdTnltenMHXhmjq9P7omMEzJraeNCavBUhdU9V2FQqFQ7FPExzk4aWRfThrZt/LaCd4+mFLyyz+r0TWBEMJSPxVw/Ig+fDFjSdhxN+7Kw2HTOeXQfnw2fXHANgIr3iSUEaIJSw31muNH1rrXOiOZ1hnWZjt98bqojRCwNFQ8Ph/PfzOLZ6+YFLb9uu3Z/LpgddTzVFCRqfToJ78zuHs7OrZMr/NY9UUZIgqFQqFokjjtNh656CguPnIIP81fRWFpOW2bp3LU4B6kJrr4feEacovCB1+e9/inZKYmMqpPZ1Zt3c2uPKt+TbzTjk3XKS4rD1lpF6zg0wPaNqdVeuhv94Wl5VE9Y3UMUzJr2QZyCkvISE6ocU9KyfJNu1i2cSc2XWPd9pyYFN3TNMGXs5Zyw0mH1muc+qAMEYVCoVA0abq0bsYVx9U+rjh/wmCe/GJ62P4+w2R7TiE7c4tolZHEOzedRkpCHNuzC7jiha8jXseSddu59/1feOSio4K2yUyrX8quBLIKahoim3blceubU1i1JQtBbGJnKjBMyb8bI5PSbyiUIaJQKBSKfYKSMjcvfjubaUvWUVjiJjnega5pGKYZUX9TSnbmFvHC5Flcfuwh/PzPqspYiUiQwC//rObooT0Z0btTrftFpeU8+9XMaB4pIHq1DKLsghIufPJTCkrKK9cQSwT103KJBSp9V6FQKBRNnllLN3DYja/wybRF7MwtotTtYWdeccRGSAWGKfl79VYueOIzvp2zvE6qrte/8i0bduTUun7bmz+wakvwbJlIeeKL6bi9VmG+j/9YSH5JeZ3VZyPJij60b+c6jR0rlCGiUCgUiibN6q1ZXPvyZHxRGh0Nhc8wOfXBD5i+ZF3ltTd/nMfs5Zti4rGYv2oLV73wDV7D4Ls5y2sUA4wGAZwyqh96EFE3TROkJMRx1NCe9VpvfVFHMwqFQqFodP7duJPPZyxmxebdxDnsjOnfleMO6UVqoqtW23d/+bveQZmxxmeY3Pjqd7x902kYhsmL386O6fh/r97ClLkrKKhH8KuuaVxwxGCG9OjArW9MwWsYlVr1UkJKfBwvXX0CSS5n7BZeB5SOiEKhUCgalVe+n8NrU+bWiM8QApLj43jlmhPp3i6zRvuhVz2HxxeZemhjogkY1bcLNl3j94VrqKPjIiBCQI/2LSgsKWdrdkGdxrjv3Akc7fd25BWVMnn2vyzduBObJhjaowMTBx2Iy2mP3aKroXREFAqFQtEkmbpgDa9NmQtQI+5BSigqdXPlC1/z/QMXVgZQSinxNrIRogkiMipMCTOWrCchzh5TIwSs92Pz7nwumDiI57/5M2pZdl0TtGmWUvk6LSme8yYMiu0iY4SKEVEoFApFo/Hur38HrStjSklOYWkNoS4hBO0yUxtpdRbHD+8TcVtTSorKohcwi4REl4OTR/Wjc8t0ApS0CYlhSi5/7it25hY1yNpiiTJEFAqFQtEouL0+lm3cGTLeQ9cE81dtqXHttNH9G3hlVQjgy1lLG22+YGia4KjBPfAZJikJcXXyuLi9Pl7/YW7sFxdjlCGiUCgUikYh0tMFuceue+LIvgzu3q4BVhRg7kaZJTwJcZY35KoXvmbx+u11HufrP5dx7mMfs3LL7hiuLrYoQ0ShUCgUjUKcw0bnVqFrmhimpF+X1jWu2W06z10xiSuOPYTk+PpleNhter36NxaGYXL8ve+wbOPOOmuIVLB0w07Of/yTkBWB9ybKEFEoFApFoxFOxVPXNI4YdGCt6w67jQuPGMLvj1/Gr4/+j9tPHxv13LomaBOmVkwsSE2MIyHOEVC7I1JK3V7K3N6YrcntNXjmqxkxGy+WKENEoVAoFI3CrrwiVmwOfURgmCZFZe6g9zVNkJGcwAkj+jBxUHcgMvVQq52gxNMwgaXVGdG7Ex6vr8kc81Qwd8VmsgqK9/YyaqHSdxUKhULRKHw2fXFE7Zas38G4AUkh22ia4IHzjmBQt3a8/9s/bNyVF3Zcn2GSW1ga0Rrqw/dzVzT4HHXlqS9msD27gOJyD11bN+OkUX0Z2K0tIlJrrgFQhohCoVAoGoVf/lkVUbtF67exZls2uUWltEhL5OihPWmZlkSZ24vdplfGeRimyeqtWVEJfjVdCc/o0YSgTbNktmRF/vw//131M9i8O49fF6zmpJF9ue30MXvNGFGGiEKhUCganO05BWzLLoyo7ce/L0ITAk0IDGny8ndzSIhzUFLuQQg4pGdHzp8wiE+mLWLqwjVRGRdNTSq+rmiaoFub5pw+uj/3vP9LncaoCIL9YuYSurVtxkmj+sVyiRGjYkQUCoVC0eAURyn6ZUqJzzQrjYyScqu/lDBnxSYufupzflsQmRHStnkK54wbEO2Smywup53TRvfn9etPZuKQA2lbTUG1rrw2ZV7U6q2xQnlEFAqFQtHgtExLQtc0jBhU0I20Gq3LYeedm0+ja+sMFq/fznu//lPvufc2N50ymknDe+Ny2NmdX8z1L/9U51o01ckuLGFrdgHtmqfWf5FRojwiCoVCoWhwkhPiGD+gG3q0WuX1oMzjpWPLNIQQ9O7Yiozk+EabuyEY0qM9px7aH5fDzqJ125h099vMX70lfMcImblkfczGigZliCgUCoWiUbj6+BGkJrqC1pqJNUkuJ3bdCmy16RqXHXNIo8zbEJwwog/PXTEJTROUlHu45sXJuL2+mM6xM2/v1KVRRzMKhUKhaBRapCXxwa1n8NzXs/hx/soGn29En041Xp8wog/FZW5emPwnhmlawbCxLpsbAxLjHPTr0ooWacn07NCCowYfiNNhr7z/w7wVIbVW6oIQVuzJ3kAZIgqFQqFocNbvyOHTaYv5898NmKbkwHaZDV7/5OghPWtdO2fcQI4Z1ouf56/k579XsXj9jgZdQ10ocXuYvXwzuhAc2D6zhhECsGDtNjQhIs4AGnJgO386dFnQNlLCoX271GvddUUZIgqFQqFoUH5bsJrb3vwBqEoZbWiFz5R4JwO7tw14L85h469VW5qkEQIVWicSn5Q89NFUmqck1DASpJQRGSG6JhjVtzNPXnIsUxeu4abXvg/YThMwoFs7enZoEaMniA4VI6JQKBSKBmN7TiG3v/kjpilrHINEcyRSl5CS8yYMrowP2ZN73v2ZGXspMLMuPP/NrBqvI40N0TSNi48cCsDYgw7gppNHo2mWPoumicrA4T6dW/P4xUfHdtFRoDwiCoVCoWgwvpy5BBNZr7orxwzpybdzl4dtp2tWzMeJI/tw9uFVuiF5xWVMnr2M+au2UFruaTKekIr1hmP9jlzOfvQjbj5lNN/PXcH0CIyo5HgnT15yLAe2y6y8dvqYgzj84AOYPPtfNu7KJSHOwbgB3RhwwN6VeBdybymYREBhYSEpKSkUFBSQnNzwFRMVCoVCEVvOfexjlm7YWa8xnHYdn2GG3LTbZ6YyqHs7Jh3Sm14dW1Zen79qC9e+NJlyr7dJyLtrAob36sTFRw2lS6sMHvp4Kj/NXxnWIBECkFbhvkiOZV699iQGdmuL12dgt+mNbmhEs38rj4hCoVAoGoxYbIBurxG2TfvMNO444/Aa13bnF3PNS9/g9vqahBEC1vtxw8mH0j4zDYABB7RlyrzwRfIq1h+p7+CTPxZy46vfUVTmJt5p5+ihPTl3/EBapTe9L/UqRkShUCgUDcaQA9s3im7IkvXbeWHyn+QWllRe+3LmEjxeo8kYIWDFxlz27JfMXr4RgAkDuxPvtBPrd2j6kvWVKb6lbi9fzlzCGQ99yIaduTGeqf6ooxmFQqFQNBi78oo47u638fjCezVCoWkiYmn3IwcfyB1nHs6FT3zW4CnC9eHK44ZzwcTBTFu8jhtf+y7i56sruibo1DKdAd3a8ds/qyn3+jigTTNOPbQ/4wZ0Q4uh6m00+7cyRBQKhULRoMxcup4bXv0On1H/OjOR0qV1BtKUrG+CHoDqvHvzafTp1IrF67dz9QvfxFyoLBCagAqbp0KPZMLA7jxw/kR0LTYHJdHs3+poRqFQKBQNysg+nfnm3vNomZ5Up/42XeOgLq0BIq5Vs257DluzCxpNTr4u6Jrgs+mLAejXuTU3nnxoo8xb3fFSEfj689+r+HLm0kaZf0+UIaJQKBSKBqd1Rgr3njOhTpogpik5rH9X3rzhFMb07xpxP4/PiFh9dG9gmJLF67dXvp44+ED6dW69V9YigA+nLog4GDaWNKgh8vDDDzNo0CCSkpLIzMxk0qRJrFq1qiGnVCgUCkUTZVD3dtx/3kQctsBCY6GYMLA7B3VtwwPnH1GnuZuqY8RWzcNj13VSk+JiHrgaCRLYkpVPcSMcDe1Jgxoi06dP54orrmDu3Ln8+uuveL1exo8fT0lJSfjOCoVCodjvOHJwD3555H/cdPJourVtHlGf5ikJ+EwrvsRu0+nYIi3qeft1br1XNvhwtEirip/Iyi9m5pIN9RJ/qy+xihGJhkYNVs3KyiIzM5Pp06czatSosO1VsKpCoVDsv0gpeeeX+bz103xKyj1B2+maICM5gY9vP5O0pHg+n76Yhz/5Paq5nrr0WPp1acW4m19rUsc1zVIS6NOpFQd1aU2LtCRueWNK2D6H9OzA7OWbYroOTQh6dMjk/VvOiMl4TTZYtaCgAID09PSA991uN4WFhTX+KBQKhWL/RAjB+RMG8+ujl3D5McOCtjNMSXZBCZ9MWwTACSP7cGjfzlHN1SwlgbTEeIb37liPFcee7IISpi1ey9NfzeDOd36MqM+Egd154/qTYroOU0rOGzcopmNGSqMZIqZpcu211zJ8+HB69+4dsM3DDz9MSkpK5Z927do11vIUCoVCsZeIc9jYtDs/ZEaMKSXf/LkMsI4PnrjkGK4/cRQuhz3k2EJY8u+9/JVlD+nZsc7rrIgziTRzJ1KktP54feHTmwXgNQwMU9ImIzlmx02XH3MIYw8+IEajRUejHc1cdtll/Pjjj8yaNYu2bQOXZna73bjdVYEyhYWFtGvXTh3NKBQKxX7Opc98wV+rtoRsY9M1/nrhmlrX3/55fq0KtWBt2gi4/9wJbM4qYO6KTXh9Biu37K6T2mrP9i24/YwxTJm3gpnLNrA1qyD6QcLgLykTEYkuB8VlwY+0IuXyYw7hoiOH1Huc6jS5WjNXXnkl33//PTNmzAhqhAA4nU6cTmdjLEmhUCgUTYgW6Ulhq9E2S04IeP38CYNonZ7MU19OJ6ugKhmiVUYyxw7rxX0f/IbPMCtjQ+qSQeOw6bx54yk47TZ6dmjJTaccxnNfz+SdX/6OfrAQSMDlsFHm8aFrGoZp1hAgq051I6TivaswZOKddrq0zmB7TiG5haVBjRuXw84ZYw6K6TNES4MaIlJKrrrqKr7++mumTZtGp06dGnI6hUKhUOyjHDu0F9/NWR70viYEx48IfKwPMGFQdw4fcAAL1mwjt7CUzLREWqQlccL/vYPXqFlvpq7nAE571ZYppaSwpLxuA4UhIc7JC1cdz8vfzWXjrlzyi8swQ6jSxjvtTBjYnU2780hyORk/oDtjD+qKw25j5ZbdnP/4J3h9ZsAg3etOGkV8nKNBniNSGtQQueKKK/joo4+YPHkySUlJ7NxplYJOSUnB5XI15NQKhUKh2Ic4+IA2jDmoK38sWlvLUNA1Qcv0ZE49tH/IMXRNY1D3qtjCFyf/ic8wY1L0rlvbZjVeT1u8jq/8MSuxRNcEHVumccsbP5BdEJnURanbyyE9O3LXWeNq3TuwXSZv3nAqD388lX837aq8npmayJWThnP0kJ4xW3tdadAYkWDln99++23OO++8sP1V+q5CoVD8d/D6DJ7/Zhafz1iM22sVyRMCRvbuzJ1nHk6zlMBHM8E48+EPWbE5NkXv4p12TjvsIM4+fAApCXGc8+jH/LtpZ4NU9k2Od1Jc5ok4zVhgFdA7f+LgkO3W77Bk71Pi4+jdqSWaEHh8Bg6bHnS/riuq6J1CoVAo9lmKytwsWrsNr2HSo30mrdLr9vl/2oMfsHprVszWpWmCZskJZCTFs6KOVX11TdC7Y0sWr99Ro6JwRfG5YT07MKcOGiG6JrjuxFGcMebgsG1zC0t577e/+ebPZRSWunE57Bw9tCfnjh9I64zY7LVNLlhVoVAoFIpISXI5GdknOp2QQBzctQ3rtmeHDICNBtOU7M4vJiu/uM5jGKbk1tPGkF1Yyvu//cPfq7eAhD6dW3HW2IP5Yd6KqDJnqo/7xOfTSYhzctwhvYK225VXxHmPfUJ2YUnl+1Lm8fLVrCX89PdK3r7xVDq3yqjz89UFVfROoVAoFPsFa7Zl89zXM7n3/V949fs5HNqvS4McndR3yIue+pypC9bQoUUardOTaZ6aQPOUBFITXXgNs17jv/zdbAwzeGDrQx9PrWGEVGCYktJyD3e8FZmoWixRHhGFQqFQ7BP4DBObXvv7s9dncO/7v/DDXyv9YmMCiUSakuG9OzFr2YaIxq+LJ6IulJR7+GZ2zUDX7IK1/LZgDQd3bVOvdezOL2bZxp0Bq/juyC1k1tLgtWwMU7JqaxbLN+2kZ4eWdVxB9ChDRKFQKBRNltzCUt6f+g9fzlxKcZkbm67RrW1zrjl+ZGWGzJNfTOfH+SsB/N/0q7baWcs2cNywXkye82/YuZx2G+VeX4M8RzgqPBQL1m6rt1pqMJGzddtzIjJwVm3JalRDRB3NKBQKhaJJsiO3kDMe/pD3fvm7sjy9zzBZvmkXlzzzBTe/9j2784v5cubSkEcwM5auZ3D3dkGl2XVNkOC04zWMhniMqNCEqFN14eq0a54a8LrdpkfU32FvXB+FMkQUCoVC0SR54IPfyCooDvot/reFa7j/g19DxkQA5BWXcdKovqQlxdcyRnRNEO90UOL2xiyoVde0OtejMaVka3YBD55/BHZbdFu0pgkGHNCW9pmpAe/369yaxDDiZbomGNajQ1Tz1hdliCgUCoWiybE1K585KzaFDTads3xTREcZiS4nH912JqcfdhAJ/s3Y5bBzzNCe9O3cqv4L9nP9iaM47bD+jOjdqe6eDSE4YvCB/PLIJVw9aQRdWqbRPCWBQd3bcunRw9A1DU3UNqji7DZuPe2woMPGOWycPW5AiGkFkw7pTXpyfN3WXUdUjIhCoVAomhyrt2VH1C4S0S8BdGyRTrOUBK4/6VCuO3EUbq/B9pwC/vf05+QVldVztVV8MWMJucWldS5Gp2uCQd2smmzFZW7Wbs9h0+4CfKZJdkEJDpuNO88cy5R5K63UX6zjnJF9OnPVpBF0apkecvwLJw6pPM7SNUu7RBNWnZrR/Tpz0ymj67Tu+qAMEYVCoVA0ORwRxjMApCa6KCwpD2iU6JpgWM+OtExPqrwmhMBh07n+lW8pKCmPaabM5qz8evU3TMlZhw9g8+58zn3sY4rL3JVHRhKYs2ITf63czEvXnMiDmRPJLy6nWUoCWfnFvP3TX0xdtBaP10fnVhmcOro/xw7rVSPTSNMEd5xxOKcc2p9v5/zLrrwi0pPiOWpID3p3bBlzhdVIUIaIQqFQKJocB3Vtg9OuV0q9hyK/OLBHQ9cEqYkubj1tTK17f63azObd+fVdZswZeEBbhvbowBXPf1XDCKnANCVSSO5+5ye+u/9CmqckMmvZBq5/5VuklJXt127P5oEPf2PGknU8ccmxtdKeD2jTjBtOOrTRnisUKkZEoVAoFE2OhDhHRHLlwXDabZwwoi8f3nZmQNnyhWu3oWtNbwtctG4bq7dkMXf5pqDBs1LCjtwi/lq1meIyN7e8PgXDMGu0r3AOzVy2gU+mLWqEldedpvdTUCgUCoUCuOyYQxjdr0ud+j58wRHcdvoYMlMTA963jiAiiC9p5JMKnyn56e+VYVcmBGzclccPf62k3OMN2l5K+PiPhTThsnLKEFEoFApF08Smazx16bE8c/lxdGmVQaQZsbom+PrPZSHbDOreLmy6bkpCHAe2a1HjWkKcI+q02miJJABXSkhw2lm+aRdamDdmR05hpQ5LU0TFiCgUCoWiSTOqT2dG9emM1zAoKfNw5sMfsiO3KGh7w5Rs2JkbcsyDu7bhgDbNWL8jJ6hB0iw5gVeuOYGdecVs3p1HYpyDgw9oS3G5h8mzl/H7wrUs27izXs8WiKEHduDHv1aSVVAStI1N1xjZpzNLNuwgEnF6WxTBv42N8ogoFAqFYp/AruukJrpIjo8L2zbR5az8/w07c3n8sz846+GPOO+xT3jjx3nkFpXy9GXH0iw5IegYG3blctlzX9GxZRpjDzqAIT06YLfppCW6OG/8IMYP6FZLz6O+pCbGMfjA9lx69LCgbQRw2uj+pCa6GN6rU0hBN00I+ndpjcthj+k6Y4kyRBQKhUKxTzGiT6eQ94WAIwYfCMAXM5dw0n3v8tn0xSzfvIslG3bwyndzOO7ut9mZW8Rxw3sHFUQzTcnyTbuYtmhdwPs5RaVhj0Wi5eELjkTTBMeP6MM1x4/EpmkIIbDpGpomEMCJI/ty9fEjARjZpxPtM1ODKrmaUnL+hEExXWOsUUczCoXiP480c6FsMtK3EbQERNxEhL3v3l6WIgBfz1rKOz/PD3pf1wRpSfEcN6wXi9Zt46GPpgJgVIu7MKWk3OPj6he/IS3JFTYw9I0f5jHmoK61smwyUxJjJguvCcHtZ4xlSDV59XPHD+SYoT35cf5KduQWkpYYz8RB3WnTLKWyja5pvHjVCVz6zBdsyylEE5ZIma4JTFNy/UmHMrJP55issaEQsgmH0hYWFpKSkkJBQQHJybXTrxQKhaK+yNJPkIX3AQagY521G+AYhUh9BqEFzrpQND5zlm/iiue/CtmmfWYqz10xifaZadz42ndMX7wuqLEgBDh0HbcvvFbJmP5defTio2oYI7mFpUy47fWwtW5CoWuCEb07M+CANkxfso4tWQWkJbo4amhPjjukF0nVjphC4fH6+G3hGv5YtJZyj4+ubZpxwog+QQvgNTTR7N/KEFEoFP9ZZPlUZP5lQe5q4DwULe3VRl2TIjgXP/U5C9duC5lV8tJVJzC0p+VVGHndi5SUh5Zaj3faKXMHT3+tzi2nHsapo/vXuPbGj/N46dvZEfQOjK6JSkOpwpshAAS0TEvmjRtOplV68P1v0648Zi7bgNfno3u7TIYe2CHmx0V1IZr9Wx3NKBSK/QopJXj/RpZ9B7IA9DYI10kIW233tCx+AStULtA3WhPcfyC9qxD27g29bEUYSss9/LNma8g2uqYxY+n6SkPEjMBT0SwlgS0RKKwK4KPfF3LKof1qyKBnJAUvEJeRHE9+cVnI45vq9yoMLOn/z+78Im59fQrv3nJ6rX7FZW7ufvdnpi1ehyYEQlhjtc5I5tGLjqJXx5Zhn6mpoIJVFQrFfoM0S5F5FyBzz4Syz6H8Jyh5G5k9EbPo8RqiTtLYCb5/CWyEVKCD+5cGX7ciPJEcnwjA7fVVvu7XpXXQIE6wPBCHH3wA3do2Dzu2BLZk5VPuqRp/W3YBD/pjUAKRU1gaVFAtEgxTsnTjTpZv2lVzLVJy3cvfMmPJesAyYCoMmp25RVzyzBds3p1X53kbG2WIKBSK/QZZeDt45vhfGVTGewCUvA6l71drHEnFVYGMqJ2ioUmJjyMjTHl6wzQ5oJpRccaYg4PHh2AVgDvl0P68dv3JEcdSVK/Z8vmMxcgQhzqaEGSmJHLREYNDGkThmLVsQ43Xf63awj9rtgY8ojKlxO318d6v/9R5vsZGGSIKhWK/QPo2Q/mPhPJwyJJXkNL/jVZvCYTTo/Ah9K6xWqKiHmia4NRD+wfV7RCAw27jqCE9Kq+N6N2JCycOBqhhCOiaQNMED19wJJmpiSS5nFw1aXjo+YVgYLd22P3CYFJKpsxdQagoS1NaHo03fvyrXtk13875t4Y37+f5q0LWyTFMyQ9/rWjSsu7VUTEiCoVin0FKD5T/gvQuAHSEYxg4D0UIHdzTww9gZoNvJdh7I4QLGX8ClH5KpdekBgJEAriOiPFTKOrK2eMGMGfFJhat215jk9U1gZTwwHkTa2WZXHHccAZ2a8sn0xaxeP0OdE1waN8unDa6P13bNKtsd2i/LrRplsLO3MKARoMpJYO7t+Xm175nR24hdptOTlFp2DVHItceju05hSzdsJO+nVsBUFBaHjb+pdzjw2ea2PWmq6hagTJEFArFPoH0LEbmXwpmDhUfXbL0XdBaIu0DwbeUSIqYIatqbojEa5Hu2WBsoaYxYqXxipTHEMIVw6dQ1Aen3cZLV5/Ax38s5NNpi9iVV4wmBCP7dObc8QPp17l1wH5DenSooc8RCLuu89LVlh7HjtwihLDquWiaQEpJp5YZvPTdnBpZLo2FrmnMXLq+0hBp2ywFLcw6miUn7BNGCChDRKFQ7ANIYzsy77xqcR1VAYOYO8H9fYQj2aBa9ozQUiHjM2TxK1D2GchiQIDjEETi5QjHgNg8QBiklNbcwoEQkelG/Fdx2m2cN34Q544biNtrYNO1GnEb9aFd81S++r/z+HXBaqYvXke5x0e3ts3JKSzhu7krABrdCAFL78RTLVj3uEN68f5vwWNANCE4cWSfxlhaTFCGiEKhaPLI0g9AlhM6wyUcOsQdhdDSalwVWioi+VZk0g1g5oGIbzQRMyndUPIWsvRDMHcDAukYjki8DOFo2rLcexshBHGO2G5hhSXlTF24hpyiUkb16cyYgw7AlJLxt7y6V+MtfIZJj/aZla87t8rg3PEDefeXv2u11TVB+8w0zhh7cGMusV4oQ0ShUDR9yqYQOI4jUjTQ2yGSbwvaQgg76JlB7wfD2qDcgLOGvkT4fm5k7nngXUiVgSXBMxuZOwuZ/ARa/LFRryfoGo11IEut92EPY+y/jpSSt36az2tT5uAzTDRNwzBNHv7kd44d1gu3tz6/e/VDE4KkeCdj+tcMmr560ghapiXx1s/zycovBsBu0zlqSA+uOX5kxIqsTQFliCgUiqZPvVJoHdZxjOt4oOrDWUoJvtUgS0Bvj9CbBR8i0JJ8m5Elr0PZZKAcRDIy/hREwkUILT38ACVv7WGEVOB/XXgjpjDRXJOiWletdZZ9jyx+DoyN/is6Mu4IRNItCL1FvcbeHygsKef/3v+FaYurCttVSLaXe3x8Nn1xg87vtNs4blgvFq/fzuptWTWycHRNoGsaj118NA57ze1aCMGpo/tz0qi+rNueg8dn0KFF2j5lgFSgJN4VCkWTx8w5A7wLqPvRjAAkiARE6gtg5iCLn/UHqYIl5344Ivk2hN4m7GjSuxKZe4bfQNojyFVrgcj4NOQmL6VEZo0AMyv8ylOeQbiODNsu4Dwl7yGLHqDy+Wusszki40uEHl7Ma3/lixmLeeyzafiM+hz51Q9NCC48YjDnTxjM5zMW8+m0RWzPKcRptzFhYDfOPnwAXVpHZyQ3BVStGYVCsdeRxm5k6TtQ+pUlta41R8SfCvFnI7To/j3LsinIgutisCqBJZ8UyNWug5bm35xbBV+LlMjso8DYEHwc5xi0tBeDj2EWIncPjGzJWjqi+Uzr6CgKpJmL3D2CGoG9e67TdQpayr1Rjbu/8Os/q7nljSkRt6+oA9MQpCTE8dtjl1Rqg5imbBL1YupDNPu3EjRTKBQxR/o2InOOhZK3QeYCBpg7kcXPI3NOQpq50Q0YdwQ4j8AyJOq1MoLHmhhg5iGLng09hHchGGtDj+OeijR2BbkPCAcRP4uZC+5ZkbWtTtk3hI6rMaDsKytg9j+GlJKXv5sd1W9Ti7TEev/2BaOgpJyi0qqfw75uhESLMkQUCkXMkfk3gFlA7Y3QBGMLsuC+qMYTQkOkPoVIuhW06t6KWJ+HG1D+HdIMIVTlW0l4I8IE39qgd4WIA8chRPYRLMDcHkG7mkhjC5YeSijclqHzH2PTrjw27sqLqOJuBY9edBQ3njyazq0ycNqt97V6bLKuWYXn6pJKLABnjDOA9iX+u0+uUCgq5c6FiN1HgfQu84uLBcMA909IIyuq+AQhdEg4H+LPtRRS0ZEiHlH2GbLkQzD3FCWrK15rfK19kPtOIhJOE47QtxMuRXr+jGA9EiIJfq01QQrh1ylANE6qclOipNwTcVtNCA5s15zenVrRu1MrTh9zEADrtmfz2fTFzF2xCSlh8IHtOeXQfnz95zI+n7EYM0K9EU0TDOvRAZcjuqO3/QlliCgU/zGklOD+GVnyFngXWdfs/RAJF4JzQlQpqAHxLougkWl5FuoQKCmEVpVmW/47svxXMDdFPU6IGcAfw2Jl1qwEY6dlDNj7gnMElicjRICjSAF7v9CzOIcgkx+DwpvDLCcBnKOjegIAEXcUsuSlEC10cAxHaElRj72v06ZZSkQKqUKA3aZx+xlja93r0roZt51e+/oVxx7C4nXbWbV1d8g6NJVIuPCIwZEufb9EHc0oFP8xZPFTyPyrwbuk6qJ3CTL/amTRkzGYIdJvdvX7BiiLX7Qk371/Y33zj0UgoQaOEQgtFemZj8w5BplzHDL/EmTuycjsceBdCq4TCPXxKRL+hwjjEQHQ4idBymMh24jE6wLKzEvfeszChzBzTsbMOR1Z/GqN2BthPwDijiTwMZIABCLxqrBr3B9JTXQx9qADwlbEHXBAW96+6TR6dmgZ8diJLidv3nAKV08Kr+UR57Dx6EVH0b9L+Eyt/RmVNaNQ/IeQnvnI3DNDN4o/39KYEHX7niKNncis0YT2GCQgMmfXuY6L9CxG5p5cp75orSxZ+ICGiwapbyI0BzL3XKxnqP4c/o0r5XGr0q97KpZj2fTfM8B1NiL5jojePykNcE+3JOZ9y6iR4SISEInXWVlGe3ipZOmnyMK7qZkBpIFwIdLeqJSml9KDLLgLyr/xt9GtObR0RMrjCOfIsGvcX9mZW8Q5j31MXlFpDc+I8P/nnrPGc+whveo1h5SSb+f8y1s/zWdLVj5gxZB0bpXOscN6cdwhvUmIC2+w7ouo9F2FQhEQM+9acP9M2FiKuBMQKQ/X+ZjGzL8Ryr8nsDEiIOFytKRr6jS2Nf7NUP4d0cWExEHS1eA6A4oehbJPg6zPYcVNyDyCelm0DGg2HeFbgSz/xgr41Foj4k9E2LoG7rMH0ixB5l0K3nlYBoJB5ZGPrTekv42mpdTu51mAzD09yNo0S6K++e9WHZ2KPsY2KP/NUla1dQbnmKjTgfdHducX8/J3s/nhr5V4/bVcBh/YnkuOGspBXWPnpZBSsiWrAK/PR5tmqTGXpm+KKENEoVAExMwaW03EKzQi9RVE3Jg6zSPNUmT+5eCZTdUm6/877jhEyiNW8Ome/YxskIWgZYas92JmHWFJlod8gCSImwgiHpzjEI5BCCGQ3n+RpZ+AZ141tdHoEWmvIeoQu1GBmX+T35gKYqzFn4OWfEftfnlX+j0xwYwwgUi62Yr5UUREmdtLdmEJiS4naYmq2nIsiGb/3v/NMoVCUY1I0111ZOmHERsi0iyG8p+RxjarjkncEYi0t8AzB1n2DZg5oLdGuE4Ce7/aRw3ueZbSqbeiiJcdGXcMIulahB7gfD6SCrVaOlrKgzUumUXPQsmLVBlH9cDYiTTzLC+D1iyqqrnS2BXCCAGQUPoJMvHq2sGknj8JvXaJdP+pDJEocDnttGueureX8Z9FGSIKxX+JuPFQsp7wUukG+FZFNKQs/QxZ+CBQBtiQGFD0ECRcgEi8Ac05PHT/8l+R+XsGTXqhfDLSMxMyvqildCrixiKLV4Z4Dt161urzlE3xGyH+56snsuR9KLzb/8qFjD8RkXhlZHVmPPMI/zNwg/ef2hkzMpK1770ibQpFtKisGYXiP4SIP42IvSIRBJLKsinIwjuxjBCwgi396qUlr1tejlD9pRtZcJu/z54bswFmLrLo8dodXaf61xfoI0wANoTr9JpzlbxO/ZVZqy+v+tFQGZR+jMw5JULV2GCy63sQyOhwHExooTIN7AMiG1+haAIoQ0Sh+I8gvauQhf8HlEfWwXl46PGkiSx+KvQYJW8gzYLg98t/tWJCgqbeGlD+I9LMr3FV6JmItDctjQ3rStUf4UKkvY6wta1aq1kEvuUh5qkLAQwnYxuy6LnwXcNojFQgfVvYM4xPxJ9LqPgQ0BDxp0Q0vkLRFFCGiELxH0B6lyJzTgb3dCLejI0wImG+lREEvnr9gZXB5thI+BNia4PfE+E4GNF8OiL5/6ygVOcERNIdVoE459DaY0SNDmLPY5a48Gst+wopy0K2ErYuYB9CWAn24oeQRQ/V7Bt3GCRcUrXG6uvFL4UfKK7Gj5TSqh5c/gfSswgp917lWYUCVIyIQrHfI6VEFtwOeAgfl1AN9+9IY2fwTS2Up6MSDcyi4LdFQmRrCiJDLrREiD8DEX9GmP4poLcDYyuRGmIi6UZLTt7zJxg7QMtAev6G0vcJfbRSbimx2jqFHj/1MWTO6WDuCL2m0neRceMRjkGVl7SkG5COIciS9yx1XGED52GI+HMQ9u5Bh5Kev5GF9/nr5fjR20DSrYi4CSHXq1A0FMojolDs7/j+9QeeRvvN1wT37OC39XaRjaEHq9mCP6A0lGEgwNYt9BgRIIRAxJ8XWWOtOSL5AUTChQhhQzgPRcSfhogb589gCW/ISDO0RwRA6K0Qzb4BvUOYljqy9KPa/Z0j0NJfQ2vxF1rmbLSUB8MYIf8gc88B3+qaN4xtyPyrkGXfhV2zQtEQKENEodjf8W2sR2dv0DvC1hYcwwh+vCBAawYh1DuF3gZcJxI8iFQiEq+pf/0bgPgz/JLnUPOjTwcc4DoTEq+HlOeRccchy77FzL0IM/tEzPzrke65SOd4IjrmKX0noiUJLRXCHOOAAd6VQe9KzwLMvCsxd/XD3NkbM+cMZPnPtWJLrMymPZViq9+/HykjLwanUMQKdTSjUOzvhBAGC4u9T8jbIvkuZM4p/s20+gat+e8/GLayr0j+P0vqvPxrLKPAL5WOA5F8DyJuXN3XX30eoSOTHwettSV5buZbeiS2g8BYDWUfVmttR+KlUunUtxxZ/j3EHWupnvrCFPYr/w5p3onQIhBiFPF1biNLv0AW3kENqXfvAmT+3+A6G5LvtETcfGvDr1nmWzFEMXq/FYpIUYaIQrG/4xjqlywvjqKTBvY+CHvPkK2ErStkfG6l2Lr/oPLYwt4XkXgDwjkk7ExCOBCpjyJ9l0P5D0hZhNDbQ9xRMa0MK42dkHch+NZgffRJ6z3xzgzQusITVOE98G/y5d+CbVCA9ntiIHcPQwoHOA5FJJyPcATJlIk7AkpeIfjRmUC4jqx1Vfo2+1OnJTWNQP84Ze+DcxjEHW7FrIRFRNhOoYgtyhBRKPZ7dKs2ihGFIaJlIFIiq8QrbF0Qaa8gjRyrmJyWhtBbR71KYesAiZfFUumjEim9VhE7Y7P/SoQ6HoHwLQnfBgAvSC+4f0a6f4KURxGu42q1EvGnI0vfB1lCbWNEAA5k6dfIsh+sjBnXKQg9E1n2KaF1UXRk6XuIuMMhEpE1pPV7olA0MipGRKHY3yn7LnwqbnWcExEZ3yJs0QWICj0DYe9VJyOkoZBmHrLkbWTehWBsIDaKo+4o2xuAiSy4FWlsr3VX6C0Q6e+Alua/YqPqO6IEfNbRkW8psvgFZPY4pGc+eBYR+nkM8PqNJlsP0DuHXqZIgLjDIn8shSJGKI+IQrGfI8s+ojLWISy6VZ3XMxpcJ1j9fWuRxW+B+yeQbrB1RcSfCa4TwsZ/7C2k9CALH/BX2G0qdT0lsvRTRNJ1te4Iex9oPt2q1+P5G4ws8Pzmv7vHsYssR+ZdbMWqhKXi52MCYY65tDaWBycCRd26II0cKJ+CNLMQWnNwHR2ZHL5iv6dpfoooFIrY4dtE5Km71qYnC24H+0AwtiDzLvFf92+IvpVWbIL7d0h9oVGNESndYBaCloIQjsBtzGJkzklgrG+0dUWGCd7FQe8K4QDXMQjXMZh5lxO8MJ+0Cu15/w0znw5Oy8Mhi58GI/jcABhrLc9R+scx/ZlKKaHkRWTxS1RUYbbqET0CiVdBwqWxyYpS7LMoQ0Sh2N/RksCIRHysOsKKWyj7Eitws7pXwf//7j+g9D1IuCA26wyAlKZVwdf9p1UozreciowaGXcsIvHyGlLugCVj3+SMkArsNV5VPB++NZYnwnkYYPgDf8MdI5WEuS8RCedbKq8lb0SwNr+h5P69VsHAcEjfZuv4TySDvTdCVEvpLn0LWVxd9t5X+bcsfhoh4iHh3KjmU+xfKENEodjfiTsOSl4mOkEzw9qQQmbaSGTJuxB/fshvtJaehUSI6ELSpGcxsuDagPLu4IHyr5Hu3yDjU0sdVBYjTTeUN2FhLr0tsuRNwI7UmkPxY/7nq350Jqj/cZINkfI4aM2ReZcS+c9eQ5ZNRkRoiEjvakup1ftXtSFaQtJ1CNfxSFmOLH4x+ACALH4B4k8P6uFS7P8oQ0Sh2M8R8WdaypyykKiCNWUZ1kdEiAwTc4elPyHSat2S7jnIkjfAMxswkbaeiIRzIe64sK546VtrqYCGDAw1QBYic84AWeBfp4OmExOyJwLKPkQGPHKpbijUd/06NPsecCBzJoGZHUVfE8yciFpaP6NTQe5RRNHciSy4xZL219uFTxuXBZa3K4TwnWL/RmXNKBT7OUJvhsj4sJoke5hCawBolpchIuy1rsjSj5B55/qNEAOQ4FuBLLgZWXhnLdXPyn7GDmTJ+8j8G7CMkHDf5E2QOVQZS01ZGbTimWORuRMKA4G04njMXKIzbPQIpftBFj3hN0ICP48segxp7o5s2lD1iBT7PcojolD8BxC2rtDsJysewbvQymAo+4jAm5QAEQeus8B7U4hR/aJneyi3St9GZOG9/leBhLY+B+cIS8iroo/0WH3Kvqi4Et0DRo0d63tYKI+LM8z9pos0CvxGYLQY4DoOWf6H5enS24J9YC0PljRzawrYBcQbQXVmP7aOdVirYn9BGSIKxX8EITRwDgfncARg2g+CwpuovZlIS4nVcTDoHf2bSaBvvSYi4X+1rsrST6ghOV4LDVnyPjgn+o8NDOvbdfl3AdbSMIiUR6y1FtwYZE47JD8Ghdc22ppig2UcYmysW3e9M+RdjaTacYreHpLvRziHVV0zdhH+fdHAyAW9i1/DJZB3SwNb97AKvor9G3U0o1D8RxHCCiINiJkDeZdC2hugt/JfrPi4sI52RNLNgevA+JYR+vjBys6Q2ROQWcORWaMs6fRG2/DtYDsQ4ToGkh8k8FGVAcUPgmNUI60pVkhE4tV19IbgzzbaI6bD2IrMu9DSN6kgIv0PA8q/8MecCGq/z1axQZFyf93WqthvUIaIQvEfREqJLH6V4BLhBhhrEMYWRLMfESmPgXMsOIZD/NmQ+hY4D0fKQIGszhDjVuCNTu01ppjIktes/y37LGgbzBx/gG+4Z9nbGhgV87sQKY8jnCPBMz+G41sVe2XR41Uz6i3APpiIthCZT4W6bA20dEh7HWHvG7ulKvZJlCGiUPwXMbPBWEtoL4QN6Z6JEE6EaxIi9TmEcwSU/wD5FyCzxyOzRiGLX61hkAhnpDLhe+vIw4DyyZi5V4N3EcG9NwZ4F0LiddT+Ru+vEpx4I1amzt4yRgRobSHp/xCZfyJcx1qXZbS6MeEwrdgi3+bKKyLpeqwtJNJn3+PnbeZC4R1IM9ZrVexrKENEofhPEmnmhl9pVUqrVkrRo1A9E8LMRhY/hcy/zhLnAnBN8tdNiSQ7Zy/i+SmiZsLWGdK/BMcYEOkgmkPcsYiML9ES/4dIexnLGNkbH6cSzC1Q/GxN1VatJQ1hHMn8K5HllvS8cByMSHsdtBZ1HM2wjn1K3onZ+hT7JsoQUSj2YaSUSM98zIK7MfOuxix8BOlbW7udWYos/xVZ9o0lFCaagdYszOi+Kre5ZyaUTw62Cqs+jdu/QWmJiLR3qhVxq/jWXPGnIan4SIvdPNK7EgpvBc+vIHNBZlveAc8cpCxDOEcgmv8KCZeBvZ9VYK6xkfnIvP8hvcsBEPGnNsw8vtXI/Msxi5615nEOh7T3QDugjgOaUPZJ7Nan2CcRMlhCfxOgsLCQlJQUCgoKSE5O3tvLUSiaFNIsReZfZRkJ6Fhn8P5slfjzEUm3Wg1LXkGWvGrVJ6nA1g3sA/ybQKCPAA1ECiJzJkI4MPMuA/c0gntSdHAMRUt/u2p9sgzKfkB6ZoA0EPZ+SPev/m/uDfSxE38FOIdC3jkxniOY2qkLkfIQwnVUjatm9vHgW0F0arb1RQfnOLS055BmiSU25ltHQ+mWiPSPQW+PzD7GMtDqM1aL5U22gKKibkSzf6ufvEKxjyIL7wTPn/5XRs2/S98GvSXSzIaS12t39q0F32awHQy+f6i50fqzGdJerJLdDruhGf42VQjhgvgTEfEnVrsYhwxR+K3uaGDvh5Z8DQAy8ao96pvUl2BGTRmy4HrQUi3vgB+ReKllJDYqBrh/RUovQkuA9A8t+fXyH6j62WnWUYq5o55z6ZZar5ZZbyMEkaCMkP846mhGodgHkb6tUD6FUN+4ZfHLUPJmkLsm4AU9BZH8kP84Ic6Sao8/HdHsW4RjYFVzLQKPpBamzDyA63gQKWEaCXAeCRk/RRHrYCKqFd+T8ZeC3i2CfrFBFj9b47WIm4BIui12E2jNwD6M8HE3BkhLhE1oKVa9mbhJFYNgZQNVVzvV/GP6DQHbgaBlRLAgw/Jsldb3WEW3ficU/2mUGapQ7It4ZoRvI/MJvYn7q7ymPIoWf1LIoUTc0UjvUoJ7BjRE3DFhlyS0RKR9AHimhmglwfMnwt3NLxEe6ojFX7cl4WJwVhVqE57fkcbqsOuJDRK8izA9S9AcVamoIuF8pLETSt8h8mMiASIepN9DZT8QXCchXCdAyYtI77ww3VOt/hWUvArlX/pfVBit1Txbtp5g7w4iCeE6GmHvi5ReZNY4MLeHWauD8BWAQ6GDiEfEN1z1ZsW+QYN7RF588UU6duxIXFwcQ4YM4a+//grfSaFQhEZ6iMxTEO6fuAQzL/wwrhMtN3zAb+S6FZgaf0rNkaVk4e9LefisZ7lu1F3cd/ITzP52PobhDT+fLESWfkzYGAutLSLtPbSkm2rIkMvSz2h0h2/uSZi7DsHMuwJZ/jtSGiBcUa5D94uFFQMe0Fog7N2tZ3OdGKav5q9ia80npdsqOhgK3wpE4vVoybdXBiYLYQfXsWHWrUHceCzNmDqidUSkf4Swta37GIr9ggb1iHz66adcf/31vPLKKwwZMoRnnnmGCRMmsGrVKjIzMxtyaoVi/8bWg/CBkJGUk9coK3Ux9cOfmfXNX7hL3HQ9qBNHXzqejr2qip8JLQnSP0DmXmCli1ZmwhiUlLQlq+j/SCgzaNHBau/z+njw9GeY9dU8NJuG6TPRdI2ZX86j97B47n/XTnxiCINEtABzZ7h3AWxdQG+D9G0EvXVVTIuxmcYNFPUjs604DfevoB+A5X2IJljUV60+ixfcPyHdP0Pq84i4w5GuU4JkmWhg64xIuKjqkmchyHDF5AzwzKp1PCLiT0eWvEvgwoMaCCci/jSksQHKf4zi+SpnANc4hL17Hfoq9jcaNGtmyJAhDBo0iBdeeAEA0zRp164dV111FbfeemvY/iprRqEIjJQSmT0hRB0YHZxjwD2V6hvJ7m12vn69Ob99nkZJoU6z1hqFeU7KissRAqSk0nC46JGzOPXm46z5fBuQBXeCt0qxM2eni7ceO4hpX5bj81pr6HpQJ86991SWzljO509+V1llNyHZID3TS1G+TmGeg1FH53Lby1XiWDWfTVDiu4hExwcgy0K8CxXHGP7jAZEC8WcgEi9H5p7tFyvbX4iD5Luh8A7/6z0+tkUyZHyDVs27IN0zkHkXEQ6RfB8i/rRa16VnPjLvkqr3t8KwFQmItFcRjkGY3s2Qc3jdHkkkITJnI4TlVZHelf6MKhs4hyH01nUbV9EkiGb/bjBDxOPxEB8fzxdffMGkSZMqr5977rnk5+czeXJtTQK3243bXVXtsrCwkHbt2ilDRKEIgPQuR+aeGaAUuwZ6W0T6p8jSD6HE+iKwYUUcN57QhdJiHdOoOMao+Ocf+Jjnvsm3MPTIVsjs4/3frq15cnbZuPrIA8jbbccwqvoKTSBNic1hw+fx0bK9m/Nv3cGIowuw+f2vi2Yl8u7jLbn95U00b13TK2L4YOt6J9cecwB3vOFjwMg1iKg8ChrYB1nHSO7voui3L2DDev8DfWTrEH82WvLtlVeksROZdWiQ9lWI9M8Rjn4B70mzCMq+rqwzIxyDwHV8ZcVlM/86f1ZO3bYRkfE1iBSr+KB3QfU74JyISHmwVnVnxb5BNIZIgx2iZmdnYxgGLVrUVN1r0aIFO3cGdrk+/PDDpKSkVP5p165dwHYKhQKEvSciYzK4Tgbi/BfTIOESRMaXCD0DkXgVuE5DSnjgfx32MEKgSmSs9kai6RqfPT7Zyr6pZoQAvPtYS3L3MEIApGmNU2GEPP/jmhpGCECfocU8/sVaNm8cjklVBo3XI/j9qzRumNSV0iKdl24XuMtARvUxZYJ3HniXRdFnX8FH8A3fgLLPkdJTeUXoLcF5GMEzbXQrSyZErRehJSESzkFLew4t7TlEwtmVhoH0ba6XEQIgzQJk7uk1VWGtO+D+GZl3sRVro9ivaVJZM7fddhvXX3995esKj4hC8V8mb3cBq/9eh6YJDhxyAElpVd8Qha09IuU+ZPK9gLcqRqLivhBIYxtL5iSzdV1ciFlqe0RMw2T5nBX8O3MTmnDSuWcZTpekrETj9y/T9jBoajN4bCEJSQb6Hp8yug1MA3odvJBPX7+av777FE032LQ6jqI8Gwf0LeWqR7ZyyIRC7E6JaepY8ZcVBolJ6PgXEQOdjKZImJgfWQJmFuhtqnok/x8y51R/ym71DV23jlhSnqwR5BsV7ml161e5uCTwzLPWHDCexwTvP9Y8cWPrN5eiSdNghkizZs3QdZ1du3bVuL5r1y5atmwZsI/T6cTprEcUtkKxH1FSUMILV73FH5/MwvBZH9R2p42JF4zhkifOwemq+rdibSaOWmNIKcEzm9WL09F0GdZ42BPTgOuP7QhAfKLBsRdkM+b4XLye8Nk4doesZYRUoOkQpxewfeV3LP/bVXl9+BH53PHqJiRUelEqj2b0DuA6Cdy/BPgGXXNuK8jyP4hw1Xypt4RmXyNL3oTST/3VhF3W8UrCRfXLWJFuKpV8o1+oVcW57BtCBxVryLJvEMoQ2a9pMEPE4XAwYMAApk6dWhkjYpomU6dO5corr2yoaRWK/QJ3mZsbx9zL+iWbMI2qD2qv28f3r/7K1tU7ePinO9B1HdM0WTpjBdvX7SQxLZGBE/rhSqjwfkjAxG6XyDolkVQZLqXFOp8+n8maJa4Q7atITAm3QWmkZuQCaf72Pm55cTNCA72anSMq/t/YiBB2pJZJpThXUBzsf8ZIqCMQAfaDEVp67TtaOiLpJmTijYAHcNTdC1Id+4FEZoRUj0fy6744D7OCikvfDtEPwARjV5g2in2dBj2auf766zn33HMZOHAggwcP5plnnqGkpITzzz+/IadVKPZ5fn1vBmsXbQi490hTsnDqUuZ+9w8JKfE8dfHL7FhfpZbpSozjjDtO5NSbj0MIDWnryYDR65Cy/puPlIJ/piXTvlsZW9fGYZrBxhSMOiY/zGgmuj2l8sRh3Ml52B0SLYSzRZa+BwnXgfvXEONqIPQGK2fTNJFg6xqyhWV81PQ4S2mAewb4VoJwWgaCrVO1+x5w/2lpzegtwDEUIfwxJ47hoLX2p1kHMgp1cE5AOA5Glk0GmQd6R6sgn/NwhNCt4otyS4C+1dnfDErFnjSoIXLqqaeSlZXF3Xffzc6dO+nfvz8//fRTrQBWhUJRkx/e+A2BQAbZTTVd49MnJrNm/joMo+YmUFZczpu3fYjX7eXsu09GJJxLu643M2RcAfN/Tw5yPCOJtGKtpksSkgxrZUL6VUCrEJqk44Fl2OwSKSH4l28bzbucjOArJJKufcosr01QQ0SCsRWcI0HvAsZGan8j1wBnzQJ//xXKvkImXY+orHocGulZgMy/BsxdWJ4KCUWPIJ3jESmPQvmPyKLH/Aq9frSWkHwPIm6sJZyW+gwy91wsT8seMSh6G0TyXVbQdMI5gRdh6wieMIaIsRMpZWy8OIomSYNLD1555ZVs2rQJt9vNvHnzGDJkSENPqVDs82RtySFUZr1pmKxftBHTlJWZKnvy0YOfkb9qPLL4NdAP5ObnNtO9v7VBa7rVR9dDp+8GnluQl2Xn1hc2EZ9gAhLdZqJp1lhjjs/jue/XsuyvhKBGiJTwx7ed+O3DpaRmpiA0gdcjiERMQGjxiPR3LXly6ymo/E6lZUDqCxE/y/6FD8q+r3wlfeuQpR8hSz5AepfXaCl9ay0DwszyXzGo9Gq4f0PmnIQsvL2mEQJg7kLmX44s/wMA4eiPaPYVxB0L2K02IgUSLkBkfIHQw9StEQnhH0vmAeXh2yn2WZpU1oxCsT8ipWTpzBUs+G0JpmHSa/iBDJzQD10PXsAsLTOF/KyCkIkh7lJPkJsWhk8yY3IRR5+TA2gkpmjc+UY5K+ZnMW1yKlvWOMndbacoP/qPgaztdkYcVcDQ8YXMnJLKtvVO4hMNRhxVQOuO1roGji7ipbtac9GdO7DZJYbPClIVAr5/txkv3+3CNFYjNIGmCf6amsIRZ4aq5Kr5jwYcoGdCxhfgXYB0zwK8CHsfS8QNG9LWHXyr+W+dz+hIcweYucj8Gy3F1GrxGdJ+MCL1aYTeCln8KlY6cJBsFWNdgOvWOCCQRQ+DczRCCIStCyL1UaR8yNK0EfGRey+0JCrjRoI3otLIUeyXKENEoWhAdm/O4u5Jj7Fu0UZ0mw4CjIe/pkXH5tz3zS107tshYL+JF4zhlRveDXo0E8n+qumSvN0V/8Qtz8WmlWU88L+OdXmUGnTrV4rNDja7ZNzJgWvVpDYzWLvUxen9ezL6uHxatPNQkGtj+repZG2ryvCRpsQEFs7KoLS0DJdrN0IE3iBFwsWVr4QQ4BiAcAyo3TThEmTB9bWvA5X1XMwsIpPB31cwQSQjc88BX4UhUe3ZvIuROWcgM77y63/UVZ9DWsdivn/B3rvyqhB6ZB6Oaoi4cciyz0O00K24FRF4q7KywuYiy78DM98S8nOdhLA3XuVlRf1p5KpQCsV/h/JSNzcc9n9sXGZJmRs+A8MvhZ61JYcbD7uHnB2BN/GJF46hddeW6Lba/0Q1XaNz3/ZhT1MMQ9CsVXXlUkn/EblktHJGcxITkI7dyyPKwnG6JMUFOuv+dZGYYtB/eDHn3LiT/iOKqL5JSlNSXurj1+/OQdgqtIN0rIVqgGZJkTuHR7Q+4ToaEq6oNg5UPrTWApH+MSL1ZSsleH9COP2eoEBGhmFV1C37BIig8GA4jKzwbcLhGGmJqgUUXbOMRJFwScCu0ixB5p2LzDsXyr62yhmUvo/MORqz8KGQR5uKpoXyiCgUMWbHhl0s/G0pi2csZ+eG3QHbmIZJSWEZ3738M+fdV1Xnw1PuYfrnc5g35R9adc7EMAx2rq85htPloLzETfM2GWRvzw0aI+JwylqZK7ouueoxnXvPEUgh6+wM2LIuriqtNgjShG0bHNz8/GbGnpiPz2dpg/h8MP7UPOb/nsT9F3fEXeavFmtKZn61mUlXT4Hy35DuqZar39YNEX8KQm8V1Rq1pGuQceOQpZ+Cb5Ul4BU3EVxHI4QLbO3BOQZZeDeUfVq3NyIiGsnr4hgB5b+GmU9C2XcgUmvHf0SLXv+kAyF0SHsLmfc/8C2jaksygDhE6hPB5ecLbgPPX9XaV/u79B3QW0LCBfVeo6LhadCid/VFFb1T7EsU55fwxAUv8efkvyLed1p2yuT9dS8CsHXNDm4Zdx+7N2ejaQLTlGi6BhJ6jzyQpTNXIISo1BXRdK2GxkgV1jn+pfdt4/iLsmvfdozkn7+u5NUb32PT8q2Vl3W7TrsDW7Nxabh0SmuON2euolUHd0DRMp8PFkxLYv1yF6dcuTtgSq5hwNTP03jy+vaV13oM7cZzsx+MYP7YIH2bkdnjaBBDQesCqc9A2cd+L0RjVAMOp6+CFdDrOgVKXq3jmgTYuiIyvo9ZJot1xDLPb3y6EfYDIe5YhJaI9PyDLHkHPLOtxo7B4DwCCm8i5M9Ny0A0n4EQKr5kbxDN/q08IgpFDPB5fdw64QHWLFgf1Z5WUmBlsXjcXm4Zdx/Z26xgTdPv5agwNJZMt7IeqseMBDZCAASdepT5g1Rr3xOOIQyc0J8Bo0tZ/89X5O0qoHm71nQ46BymfbGLh854JoKVCx65oj1PfLUWiaxRS8bng8JcG6/f35rnflgT0AjZvc3O9+9lMPeX5ErFV6EJ+ozsEcHcsUGaJcjC+xtodAFxI9Ac3cHxf5h6Zyh+oIHmqk44w8JfEDHhQmT5T2BsJuAxjnO8pWBbC6s2kUi6M6bptEIIcA5FOIfWuC5LPkAW3UeNgFb3NH9V6TCYOeBbEbKWjqJpoAwRhSIGzJ48n1Xz10bVRwho190qdf7l09+xe3MA70Ud2bgyjo+fzeScm6qrUlqCVjLuCMg7Bzzz6NxFhy6WeIfM/oS8LUdHPMeaJfFcObEbZ1yzi1HH5mOzQ3mZ4NdP0/n4uRZ06FaOK6H2xrhoViJ3n9MJr1fU0DSRpgyhOVI/pJkHpR8jy760xLlECpjZNJxYlkTYule+EgmnIktf8c8ZguRHwdhiBZu6f2yAdZkI12kILRkyPkEWPQFlk7F0QACtNSLxcquQovsnZOEjNev26B0RyfcgnMMaYG01kd6VyKIKQ7G6sRRFkK0MnVmmaBqooxmFIgbcdewj/PXjwhBeisDc9M4VbF+zkw8f/DLma0pM9fHxwuU4nJIta10smJ6C4TiJ7n2X0bPvjFqZKT9+mM4zN9WtyKTdaRKfaFBSqOPzWi6QwWMLuf/9DTXaFebqnD24B+5yDRlElfWRn+9kwLjAcQF1Qfq2WhVegxZXayCaz0LTM601eBYji18BT5hv8iIekXgl0nU2ZA21CtnFEr09otkPNYojSrMQjE2A0zpyqRb8I6UJ3gWWd0FvBbY+jSYsZhbcBWVfUPfsHhsi88+IBd4UsUUdzSgUjUzervyojRChCcqLyxvECAEozrexemkHPn0unr9+s1uaD2IWpinp2KMrd766iXZdLY9AabHGy3e1IRqF1ep43RoF7ppnMOv+dWEalnZIBT9/mo67TAsqN6/pGl8+/X1sDZGC6/2eiEY0QgCyxmC6jgdbdyi6n4iSFGUpsugxhPQiXSdD6fvUfSMOQMIltSs0a8mg9QnYXAgNHANjN380eP6h7s+uQ9xRygjZR1DpuwpFDGjZKdMKLI0CaUo+e/zbBv2G+dxtvfn7D6u+iJSyMvZk8+o4bji+C7l+nZGZ36XiLrfO/2NFzk47s39OweerurZkdmJI9VTTMFk07d+YrUF6l4N3ETHdzCPGA2WfQ9F9WAZe5GuQxS9C/Hlg60zg1Na6oCGco2I0VsMgpUSaxUjps+oFhUVQ+/3RLM9P8u0NsEJFQ6AMEYUiBky8YGzUHhGAXZuyGkzvICElnk3/bgm4LtMQFOXb+PbtZgDs3OJAt8V+Hc/f2oZdWxwYBvi8UJATq001QryLGne+WtTVC+NFeP5EpH9ipaCK6q7tuhiMwspCCZFyK6UX6dtoZRJJA2nmYhY9h7l7NOau/phZE5El7yDN2NfxkWYRZtGzyN1DkbsPRu7qC9JH6OfUIW4SxJ8KIt66pDWDhMsteXnlDdlnUEczCkUMGDCuL8MnDWb25PkNYlhouoY0TYSuYfoi2NwENGuTTllxeVADyTQEv36Wxnm37CQpzReikm7dyc+2c/WRB9BrUAnL/0mgKC/0R46ma/SNaebMvvpdSwMzB6ElIZJuQiZeZymHChd4FyHzrwVZEPlwIh2R/H8Bb0nphZLXrcrGpl9iXzQDyv3FA/2/P8YGS9q97CtI/8A60okB0iywYnh866vmwucvahiixgECkXgJwtYZkv8PKY2qysCKfYp99V+pQtGkEEJwxyfXcvKNxxKX4AzfIUpMwyS5eQpHXDCGNge0pHXXlow+9ZCgc408cRiJaQlhvTQVdWYO3UP4LJYUF9iY91tKWCMErOc88brIM3fC4jiEWB43NR6GJcjlRwgbQm9m1dYpeRNkGVF9j5QFBFKgk9JA5l+FLH62yggBkNkgi6np0ZHWH98aZGHstF5k8TPg20Bt75FB1c+u+s9QA2yI1OcsI6RiHM8/mAV3Yxbcg+meq5RV9yGUIaJQ1JGivGJmfjWPPz75ky2rtmF32Ln40bN4a/kzlWm5saRgdwE2h53n5z7Ma0ueZMvK7ZSXBEg/FZC9NYeWHTPR9BCbsJBktrXSG9Nb+Bg2vpDq30CFkHTuVUavwcWkNY9OEjzaeJkKzrvvNAZNPKhOfQMhbO3BOZaYxFnY+lh/GsOwES5Ly6MasvhlZN6F4JmDlW7rC9g1MD4wi2pfLv8R3L8TnaCbAeXfWSnR9USapVD6JcHjZ/zrijvSEjKzD4KEyxDNpyLiDgfA9K7F3DUM8s6yhOPKPoa8c5C7h2N6NwQZV9GUUEczCsUemKbJjvW7MHwmLTtl4nDWVGb0ery8fssHfP/KL3jdVZtBv8N6cdNbV/DMpa+ybe3OBlnb5Bd+ZMprv6JpAk95EONAwoq5q8neloNpBN9gBIKjzi7ENOGZm7oy+6cEhBBIKRl7Yi7n3LSTlu2tOUwD5vyczCv3tGH3NkfQMTVd4Ix3cs49p/Dqje9F9WxCExxx0Zig9/N2F7BhySZsDhvdB3XB6YrM8yRSHkHmXgC+JVQpj0agQFod+0C0jI+Q0oPMuxw8MyLvWwdE0m0ILb7ytfQsRBY/7X9Vl8BbJ2gpta7K0o+I+r0AwIfcPRzpGIZIuCDiGkC1MLYA5WEa2UDvgJb6dK07ppENOccRsHaOzIacYzGbT0PTM+q2PkWjoAwRhcKPlJLvX/2VTx/7hl0brYJeCSnxHH3JOM6+52ScLidSSh456zlmfjWvVo2XpTNWcMXgWynIKmzQdfo8kX0TztoSSFnVQtM1uvTryJHXvc93b/3Gzx+/C1jvwfEXZXHpfdtrFLXTdBgyvpCeg0q56ogDyNruqHZPYBoSp8vB4WeN4oiLxjL1o1lRP5cApn4wk5NvPLbG9YLsQl68+i2mfzGnMj4mPtnFidcezZl3nYiuh/Z2WOJdH4N7KrLkI2vz01It0a6iZ4Fw3+xtiOQ7rLGEA9JeBffvyPwribk0vJaJSLoB4Tq+xmVZ+gE11EWjQgfX8bXSdgEwAh2JRIoPPLORnpmQdCuiLnVdRFwEjSRCBDE6C+8ldAE/NxQ/BSmNVzZAET3qaEah8PPaTe/z3OWvVxohYEmwf/7Et9w28UE8bi/L56xmxhdzAxaaMw2TwuyiBlMHjRVCwMQLxvD47/fgdDn44qkfKu8lp/u46K7tVrs9Ph1sNkhK83HWDTW9PaYhcSY4eWzq3aRmpnDtyLv4+tkpUa9L0zV2b6mpPFpSUMJ1I+9i+udzagTplhaW8cH9n/PEBS9FFgsgi5ClX4B3rlWB1vcvFP0faC5CH7UISHsbYe9VdUXoiLhxxPbjU0Dc8Yjm02sZIQB4F1JnI0RLQSReFmTapDqMWR1rTbLoEaR3RfTd9fagdyT0z8DwH68FwP1H+DnKfwjfRrFXUYaIQgGsXbiBL576LuA905Qsm7WCn978nV/fnYZuC/7PRkoZUiejKSAlLPp9KVcNuY3bjniQ3ZuqDK+xJ+QFrA1Tgc0GY07Ix+mquSm6S9zcMPoePnzwy4g9NntimpK0FqmYpkn2thyyt+XwzQs/sXXNjoBBt1LCb+/PYMW8NSHHldKNzD0XPH9WXKk26U7ATu2NUAM0ROpzaM4hgQe29yG6j9A4wEXAeBWRbEnPl30ZJD02gsJtIgGo7vUQ4BiGSP8seOXiuKOJzTagI0s/jrqXEAKReAXBPUsaOA5F2LsFuR+BhLsMd/Sj2NuooxmFApjy2q/oNg0jSGqslPDFU9/SoWe7oG32Jbavs2rQbFm1vcb1zLYeDEOg6cGtKYdTktbcx87NNTdUn6d+omGmaeLzGpzd5cpK48hKWw6+Ft2m8fNbv9NzaLCNCiifAr5VwWYFPOAYDb6VfsNEA+dhiIT/IRzBA2dF/LnIguvCPldl+8QrIO5IZOm7UPY9yEKsgFNhZbV4piM906D4WUh/F2HrWtXZOcYqbR/UK6JD/FmIxKss7RRZBnpnhC20ZL+IP8M69pGF1E951rCk4KNEGtlgFoJ9GHjnUKWRIqwx7YMQAWJDqnAQ1hgRCVGvS9G4KENE8Z9BSsmm5VspyC4ks30zWnWqEnfasmp7WANjx/rdJKYmhDRY9nUWzUrkuAtCF2aTJhQXxFavQbNJJp7poHj7ixw8XGNOcTIFOfaw6ceGz2RXNY9OIGTZN4QOyBRgbkc0n44VOGlHiAg+GuOOBM98KPvIGiNkSfr2SPdMMDYiXCcinWMg77yKFdb828xF5p4PzX+rjI2oNBgwA8wjABsi/nQrDsQxOPzaK3rqzZCpz0PeuRH3CU7kaetSmlbwbckbWM+kU6mSausBjj6IuKPAPjC08rBzHLjDHAPGHRfxuhR7B2WIKP4TzPthAa/f8gGb/t1Sea3PqB5c/vT5dD2oE4lpCWiahmmG3vjWLNi/0wH//iM5kNxEJYYP/pmeRHFB3T46jrtyIjO+mEvezvzKa83b+Bg6roixJ2ZzQL9SdB2uehgmv9WMNx5oXaNC755oukZqi9rZIDUwcwj9bV9a4mFCYB2dRIYQApLvAecwZMkH4FsKUliBsNJjSZTLcpD5YG4DczN4/7EqAGvNCR58aoC5C8p/Ape1iQpbO0h72crYwU2VMSKAOETaywi9jinjJW9T/zo8WmU6bSTI4heg5NVqV3xVf/uWIhLOQTgGhR8o6TZw/0pwr0giJN0Y8boUewdVfVex3zPzy7ncf8pTADUCGzVdw+aw8czM+9m2ZgcPnv5M2LGEJohPdlFaULbfCiadc+MOTr92d61YEdMAwxDcMKkrqxbFB+4cCgEfLHbSrP1A3rjXyVfPTUOaJppuIqXANAQ9BxZz/wcbSEw2MU34/r0MXry9bdAh45MMnv+jL207rLAkwe29EPGnIew9q9addwm4ZxD8WEMDe2+0jC+if6YQmHlXg/sX6rbJaxB3FFrqkzWuSjMXSr9AeuYBAuEYAvEnIrT0uq3R/Zelv1EvBAgXotlvluhaGKRZhNw9jJBHKlorRPM/alQCDobpXW95dMxdNW/oHSH9QzS9edgxFLEnmv1bBasq9mt8Xh/PXvYaElnLcDANE5/Hx4vXvMXw4wfTsVe7sFpV0pSU5Jcy6Mj+TT47pq5M+ag7v37eCsNnHcMY/i+reVk27jq7U52MEE2XHDyqkObN/mLOVx/yxdO/YxomUoLh0yq9Hsv/TuTMg3sybXIqmgbHnJNDi7aBN6yJZ+Ty+b//0rb1R1ZWiW8plH2KzJmELH6psp1wnUzojBMT4Tot6meqQEqJ9K5AuuchDSvmRvq2gvtn6u5pkCBrp6UKLR2R+D+09DfR0t9AJF5cZyMEsOJR6o0DnIchSz9CeoPF4lTD/Qdh4zrMHeBdGtHsmr0zWuZMSP8cEv4HCZdCxk9ozX9RRsg+gjqaUezX/PXjQgqyAyhK+jENk3//XMXuzdk89tvd/K/fjeTvDl/D468pC/dN5fAw6Had15c+ywtXvcm7j01nyNh84hMNtqyNY/4fSSGPSYIhhMTpMrn4zu38+WMyT9/YFutoIfBY5aU6D1/WgbwsG8eel82hx+Xz2YuZ/sGsrmNPyuPax7cEGMIyNmXxM2DriogbD87DwDEKPDOpHV+hgb0vuI7dc6CqEY1dlvaISAbbATViFmT5T8iiJ8HYVHXNcQjYBwaYKzqEvU+9+odDmvngnV/PUXTADeU/AxJZ8gLSOQaR8iRCCxIkahYSNqYGQAb/dxsIzdEPHP2i6qNoGihDRLHPUJhbxK/vTmf53NXs3LAbr9tLYloCB43pwxEXjWXXxix+enMq29fvIrV5MmPOGMnOjbsRmgiZeQGwa1M2B4/tw4UPncGTF70c2YL2w5OZZm3SiU9ykZCSQH6Wgx8+qL8iZauOblp1cHPVUd3weSJ3wr5+b2uGTywgOb0qHXjc2YfSf3QHxk64JYxHSiBL3kDEjbcKoaW9ZBkMpR9TpeTpANcJiKRbA4p9Sd8mZNFD4J5G5Q9b7whJ1yPiJiJLv0AW3k4ta8gzDzz/RPycgdYOdog/sR5jRIBZHEXjiuwUG5Z3qSL4t1qRugrc05AF1yHSXgs8lK0DEf3j0UNn/Cj2H5QhotgnmPbpnzx23ov4PL5aRyxLZ6zg/fs+R5qyMqNF0zVmfDGXVl1ahDVCAJIzEgEYfdpwXr3pPUoLSjEj6Le/sWtjFqe3u4Thxw/B8AU/zhAadD24M2v+Xh9iNIndKdm+IY7tG5xE60IyJfzxTWqliqtu18ls34xxJxcgi8JplUirSq0sR4g4hHAgkm9DJl4N3mXWfXvPoBVkpW8zMudk/7fyar8HxiZk/tXIpLug+ImquWpgBLgWiAqvQPWMHisbSaQ+Wb8jl0jQm2Npm4TR2bD1QKR/COW/WOnNWgaybLI/XTfQ0ZNpGSPe5TVidSpxHAJaCzB3E/h90sF+MMLWIdonUuyjqBgRRZPn39mreOjMZ/F6vEEDRCuMjYq02oq0z50bLI9IMISAtt1a0aVfRwDi4p3c+/XN2J12tBDCZfszebsK+P6VX8honRaweJ3QBDa7jSueGMZZN+zkoru2c/jJuThde25KAq9bq/z/aNE0ybYNTv74OhUAaZgkJMcjjXWRjydrGlNCS0A4hyCcQ0EkIn3rkd6VtUTEZNETfiNkT2PM//tX9Ii/Am4wKt6LYL9DOjiPRqQ8DvbegMPSu4g7GpHxJSJuQmTPVw+EcEL8CYQuCKhB2hsILRERfwIi8XKImwjevwkd/6Ihy38KMq+OSHmICtG4muggnIjku6N5FMU+jvKIKJo8nzzytVWMrQ4einB9pISLHjmrxrl/31E9eW3Jk3z17BRmfD6HksIyPGURKDjuZ+Rsz6PX8AP598+VaLqG0ASG16BFexfP/1JOcuLNdLtWYBpgs0suv38bj17Vnnm/Wum0qc29FOba6hRXYiFYu9RVmSpsSsnIk4aCiCAgEkDvGDBOQUppKZiWvATGVv/VOGT8iYjE6wEjgmwXLxEVixPJAcTCNLB1QqTcidDSEK69p3MhEq5CuqeDsZOaRpflrRHJ9yH2DPiUgZRf98TvFUm4EFGt2J408620ZDML4s+1jrB8i6vmdI5CJN1UU8xNsd+jDBFFk2T3lmxW+qW7501ZUO9U2SFHD2Dhb0vwlHvRdA3TMElKS+DK5y9k+KSaAlCbV27j44e/Ytonf+LzGtidEchrV0PTNc66+2Q+euALDJ+5z6b56jaNjj3bcu0r/2P25Pl4yj107tue4aNfQfiWWG10SUXNOVeiyT1vbuT6SV1ZtSiB/Cwb9YnoNXyCdcusDB2hCcafO5qWHTORnvHIktfD9g9WhE0WPw8lL+yxtnIo/QTp+RuS7yeybJcI2qS9Ae7foOwrMPNBb2Fl6MSfjtASI5ijYRF6BqR/jix6Csq/pTKbxdYdkXh1YG0QLQNEfHiDxLcKmXMKZHxmGWQlr1rvPT6qNFTsVpaLczxCbxlR+q9i/0MZIoomRX5WAc9c+hqzv5kfsw1c0wQHHdabU286lrfv+oRNy7ficNoZddJQeuwhDb5q/lpuHPN/eN3eymMerztUdc/amKbJRw9+SVxSHKbPpLQwlAu/6WL4TLat20nHXu2s1Gb85ehz5wZsr2lgmHDGdbuZ9tPZ/PHRn/X4GdbMqhl3zqFc8/LFAAhHPyszxTOXoMaAfTi4Tqk9qm+j3wipmKM6BvjWWLEQ9UYDex80R19w9IWk62MwZsMg9GaI1IeQ5u1gbAcRj7AF124RwoF0nQylHxA6LVqCsRlZ/CJCb40sfqravYoYHw+UvILQWyAcvWPwNIp9ESVopmgylBWXceWQ29i6OnCRs/pwwrVH8dUzU2rIs2u6hqZr3PPFjQw9egBSSs7rfjU71u+q0zHQ/oamaww/bhAjThzKynmryd9dyMAR0xl97HJstuDvj0SgtVjENSMeYNVfa+v8s7Q5bYw5fQRn3XkSrTq3qHFPmkXI/KvAM5uaqaDCqrmSdGdAaXCz6HEoeYuQG6iWCXpbf8XbuvweWLVSRNrbCOewOvRv+kgz3/J2GJsI/x7F+1VmQ6TjilRE5qyA2UuKfZNo9m9liCiaDF89M4VXbng35kcZjngHntLAMR5CgM1h4721L7B19Q5uGntvTOfe17HZdXzeqk1b0yQnXrqbC27fGbJKr8icy8yvVlUq2kZKh55t6T3iQPqO6smIE4bgiAu9MUnvEmT5zyBLEXoXcB2H0IKXtjfzrvRLgof5HUt7G/IuCN+uBv6YES0dkfwgIi5I6fo9kEY2svRDKJ8MZhHYOiBcZ4DrGISI7liwMZFmHjL3bPCtjsl4luE2PCZjKfY+0ezf6mhG0WT44c3fkA0gzhHMCAH8yp4mU177DVdiXMzn3mfxOxmqGyEApin4/KVMNA0uuH1nkL7JIJIZeeJQJpx/GD+//UfEc6a1SOXaVy6JfJn2vgh734jboyVjGQyhjhScCMchSOdovwpoJOiQeBXC1t0KuIzQgJC+tcicM63quxXHTN5lSO+tVsxG2quVhe+aGkJLs94j3zpCv58RYoYXElTsn/w38xMVTZLc7Xlhv4AGSietL6ZhsvD3pfz8TqSbzv5LxWlG6BBTwZevNqcgJ1DapwbxpyKEDSEE5SXlIdOnayChILswugVHiYg7ktCbpm6l0AqBSLg4ipENyyiKGxu5ESJNq4hdrawa//975lrF4ZowIm4iod9PDfQAWiKBULoh/1mUIaJoMqS1Sgu5AwpNYHc2jBOvIKuQzSu2NcjYTYII/qXbnTbOf/AMTrttEuFOx3w+wcwpe1a91UHvhEiwPBplxWXM/HJexPE2mk2jzQGtImpbZxyHgP0gAmtnaIAdkXiR9dI+ABxDiTTzR/qirMzsmQPGRoJv5CaUfoiU7ujGjQIpy5Cln2Pm/g8z9yzMwoeQvnUR9xf23uAYSeBfML9bLelm0DsHaYN13dYNbBEaLIr9DmWIKJoMR1wwBhHiQ1+aEneIY5a6IgTs3Lg75uM2KcLJXWiCE645itNvPZ51izZGNOSmVVVHWeWlGsSficj4pFKttCi3OKpAVdNncuRFkcVV1BUhNETa65ZBAlgGid+41TIQ6W8jbF38bQUk/R+ICBVOi+7HzLsycsPBu5DQYmKALIZoDZwIkb6NyKwJyMI7wDMdPH9B6fvI7COQJW9EPI5IfRYcI/yvKt5PATgRKU+ixR2CSHnAf2/PLUcDbIjkBwIGFyv+G6gYEUWT4ciLD2fKa7+yY/2uysyWCjRdo2PvdqxfvClI77ojJfg8MTjj3odp0aE559x7KhDh8ZcUfPt2c2b9kEp8IvQedSg3vHldjSZJGUnoNj2kVHwFQsCok4YxcEL/uiw/KoSWjEh/06oU656GlG5Litw5GiGqPhKlsdsqLy/zIx/c/Ruy8D5EyoNW0LX3H+uIRZoIxwBwDKtW2j6MEVJJpO0iR0ofMu8CS1jMuuL/2/pZyaLHLEG4QDoieyC0RET6G0jv8qrAYVsXpH0osvxbZNZES3PE1hsoA9/Kqs6OoYikGy3PiuI/izJEFE2G+CQXT824n6cuepm5U/6p/GzUbBpjzxjJWXefxHndrq5Xam0kBfD+izRrk86KOavZsHQzWqh0mD3I3WUndxf0GxtPzo48MlqlVd5zJcRx6CnDmP7Z7FqGZXUSUuI58bqjOeP2Exr1W7Gwdwd796A+OFn8HJjZRBeIaULZl5iu06DwLvAtp8KQkCWGVTQv7SVLOdRxCPB0mEUmgK1TFPNHiHtqNVXZQGjIktcjMkQqEPaelbVlpHc55J5keXQq3HFmFmCA80hIuMDSDtFbBB1P8d9Bpe8qmiQ7N+5mxdw1aLpGn5EHkt7S2uDuOf4x5n7/T521KfqM7EHW1hyytuRE9E29qRJrg8rpcuCuh4y9pglsTjv3fXMzA8ZVlWLftnYHVwy6lbLi8oA/s/HnjuaaV/6HI0r12oZGyjLkrkFUKo1Gi0jzB6Hu+Tumg5aCyJgCWjpy98EgS0IMZEdk/hVQqr4+mAV3QtmXAdZXE9FiMUK4ohpbSg9y9yiQeQSLPhfJ9yDiz4xqXMW+RTT7t4oRUTRJWnbM5LDThnPoycMqjRCAS586l6S0RIRet2/OUkoe+vGOfdoI0XQNm8PvzIyRA6E+RgiAaUq8bi93T3qM7G05ldfbdG3FM7MeoPvgmrVDElLiufDhM7nxrcubnBECgJFFnY0Q8G/CgX7HDEvqvewTMLaFMUIAvH7dk1gT4e+/jP7fiSx+FWQuoVLgZMlb+2zpA0XsUUczin2KVp1a8OL8R3jrjo/445M/o/YKLJu1ktdvfr+BVtc41PAsSCKOw6gvrsQ4UjKT2b0pC9Oo/b5LU+Lz+Jjy2m+c6483AejYqx3P/fkgm5ZvYfOKbcQlxtHv0J5hxcr2KiFE0eqPiSz7FuGIRHVVB2NXzFcg7H2RZV+GagF6O+toKAhSegFRM67GLIWSV8IvwNgCZg6o2jIKlEdEsQ/SokNzbvvgGr7Y/SaHHDsQIHKtCmDOt3831NIajerGSGMYITaHjQ82voS33BvQCKm+rvk/Lwp4r0PPdow8cSiDJvRvUkaINAuQ3n+Rvg2V39KFlgb2yFN3q7BSgMNPWghaJJuwEWG7KIk7xipcFzxCBhF/rlX12rcZs+gJzLwrMPNuwix8GDN7EnJXL+SuXpg5ZyDLp1rdyr/HqkwcCSpLRmGhPCKKfZbk9CT+7+ub+fvnRXz3yi+smLOa/KwYC2JVL2PyH8bn8bH67/URxeYY3n3j2Esau6zskPIfqSzCpneBpGtAJPmzO6L54esgnGDrB96/CH78oVl6K7b2SHs/8C4leH61E+LGR7GGyBBaIqS+gMy71L/OirX6ZeqdEyH+DGTxa8jiJwmuRivBuwCZ/zckXoc0NleNEQqtPWgRpkUr9nuUR0SxTyOEYNDEg7jvm1v4fNebnHXXSTEb2xnvZNDEg2I23t4gllkoUkr6jOyBbgv+sWEFF/eI2ZwNhTSykDknQ/kPVFWCBYz1yPyrkXkX+mXXgxFnVfgVif7Xdog7DpHxDSLxEkLHYJiI+NMBEEk3U1EkLxAi6ZqQtXPqg3COQDT7BlwngUgB4sDWC5HyGCL1aSj/EVn8BJYxFvp5AGTx01b8SySejoRzlW6IohJliCj2OiUFJSyfu5o1C9Zj+AyklGxds4NV89eSt7tqMyjOL2HZrBUsn7MKT3ngQMJz/u+UiI5pQn0GCg1GnTyMr7Lf4v7Jt9Cxd7uQ7ZsKQggcrqojD03XGHniEB768Q6OuHAMrbq0qLNEvs1ho/ugLky66siQqbhSSo65LPbf4OuDlAbSt9kS8JLWsYEsfr4qnbRma//fJqG9IW7w/gnxFyEyFyBaLEFLfQRh6wiOYRA3KUg/AY5REHeE9coxCJH2Buht9miWiEi6A+IvjOZRo0bYuqKl3I/WYj5ayyVozb5EuCZhpe++QnTHJzqYJYQNhBXpiPiz6rxmxf6HOppR7DWK80t47eb3+e396Xjd1rfShNR4nHEOcnfmA1bsx5AjD8aV6GTW139VtUuJ58Rrj+aMO05At1UJPhXlFkc0d6iAfWnCKTcdVxnH8Ogvd3FWpyvwuiM9+947aLrGuLNHMfasUZiGSfsD25DWIhWAQRP6s3Pjbs7ufEWdx01OT6LPyB5c+NAZvHn7R+g2rdIo0W0apiG5/vXLaNe9TZgRGwcpTUsptOQtMHdYF7V0pOsMKPua+hVq8/8ClTwH8SdYRx1+hBCQ8jDYu/nnzvbfSIH4sxCJl9UI8BTO4dDsN/D+Db4toKWAcwRC7MUijObuOlTVNSzxNy3TCkQN9v4m3628IYoaKB0RxV6htKiMa0fcyablW+usCQKQlJ4IUpKYlsi4sw+lY+923Hfyk/Va2zn3nMLZ95xc49oprS8mz28cNWUccXY85V7iEpxMOO8wTr1lEs3bZgCwYdlm/tf3hqjH7DmsG4/8fCeuxCo9icXT/+Xr535g6YwVCE0wcHw/jr/mKLoP7BKzZ6kPUkpk4Z1Q9nkDz6QhEq9CJAY28KT0gbEJK72pPUI0nSDdUEjfFmR2HeT27Qchku9D5p7jV6St2F50wICEK9CSrondQhVNlmj2b+URUewVJr/wExv/3VJvUa4KD0hRXgkfPPAF8cnRiS8FolnbqiC6suIyfnt/Bp4IdTYy2zcja0t22KJxDYWn3PLalJe4+e7VX5j26Z88O/tB2nRtRYsOzbE7bZVepUjof1hvHvrxduyOmpkg/Q7tRb9De8V07THF81cjGCEAAmlsCXqAIYQNbHUzzqSxDVn6KXiXAA5E3KFWHEo170uDobewPDgh42T2RCDixlmKtc1/gbKvkOW/+OXdeyESTkfY+zTYkhX7LipGRLFX+P7VX2IutW4aJqWFZVGl8tZCwNfP/QDA7i3Z/K/fjTx35RuUFJSG7Xrbh1fz1opnGHni0LrPHwW6Ta+K+QjwyKbPpCivhCcueAmwJPQPP2tUVO/PmgXrKc4P/+xNDVn2CQ1Ro6U2pj/QM7bI0q+QWWOh5DXwzAbPdGThfcissUjvipjPtydCOCD+NCLfIjQQyeA60eqvpSASzkfL+Bit2WS01IeUEaIIijJEFHuFrK054RvVAdMw62fgSNiwdDNer49bxt1vVeUNNZx/Tz/6knEcdtoInC4nd312A3d9fn3d11B9eGEdt1Sf76Cxvbn0qXM5+YZjOPqS8ZXrDoRpmCybtZJNy7cAcP6DZ2CPQsm0tKiM7176ua7L33v41lK/GBCI7ONRgt6hnvPsMaJnAbLwNqyA2YpjS2n9kQXIvPORZjhF1vojEi8He1+CB6wKKt8jkYJIf8fSX1EookQdzSj2CgnJ8RTnN8yHqaZrtGjfnB0b6qZIKTS4uM/1bFu9I2zbFh2ac8qNx3HMZeNrBOCNOnEY7Xu0YfOKbXVaQwVSwp2fXk9SeiJlxeW0696alh0zK+9P+/RPvn3pp7DjrF+ymQ4925GWmUJmuwy2RvBsYKml/vzOH5zzf6fU+Rn2CiKZ+onACNDbg7ExfFPvIuCMOs5TG1nyFsF1O0wwc6H8O7/HouEQwgXp70Hph8jSD/1F8pzgHGdl+ZhbAc1SiHUdFXVNGoWiAuURUewVDj9rFFoIPYr6MuH80TVSWaNBt9nYvnZnRG0veOB0jr18QsAsgOteuxTdrgf9QnnRI2eGjGkRmqDbwC4MPvIgvG4vv7w7jQdOfYrbj3qI3z+ehdfjjfgZq3tVmrXNQIvieKYwwkykpoRwHU29leiEi4iOd8wYS7C7pxPamyOQ7hmxnTPYTCIOkXAhWvPfES1WWGnKaU+hJd+Alvo0WuqTiPiTlBGiqBfKEFE0KqZpMv+nheTszEMgGkTl2TRMBh95MMdfdUSd4kV8Hl/ExzsfPhi8Xkfv4Qfy+G/30LFXuxrXm7fL4I6Pr+XUmyfx8j+PcdCY3kH73//tLTx69vPcfPh9zPxiDqvmr+Ofnxfx8JnPcuXg28hsn1EjfTkQjjg7/f1z7NiwC2e8EzPS4ysBme32wXogcceC1obAhoRO+I8+CWYeaM3DtNNBa1mnJQYn3JGSBNn4qeRC6CrtVtEgqKMZRaNRlFfMHUc9xIq5a2poUAAxk1LXbBo9h3bjgIM707F3OzYt38rc7/9BaCJi40KI0Doj1dm8Yhsblm6iU5/AcQJ9RvbgtcVPsm7xRnZvyialeTI9hh6AplkbYesuLXnst3vIzypg6gcz2bFhN0lpCQw9egDdB3Xl/fs+Z9qnfwJUvl8VRsSGZZu5fOCtoZ9LwKSrjiQhOZ5vXviRl659O7IHq+wuOPqScVH1aQoILQHS30fmX+rXw9Cxfsl8oLe0jAfvIkLLsHcAxyAoeYngkuUGwnVCbBdv7xVG9l1DOPrGdk6FYi+idEQUjcYtE+5n0e/L6qUbEowKQ6N9jzY8PvUe0ltaQXOmaTL3+394/ZYP2Lpqe9hx6lLJ9p4vb2TE8UNwl7mZ/9MiCnOKadmxOf0O64Wu1z1zw+P2cmqri+sVSzP+vNFc/9ql/P3LYu48+uGo+mq6Rqc+7Xl65v24EvaiuFY9kFKCZx7SMxcwEY6DwTES3L8j80OLu4mUp8A5HJl9vP/4Zc/fCwHOwxCpL8dWSr/se2RBqGBnG6L5Hwi9RczmVChijdIRUTQ51i3eyIJfl8R0TCEgpXkyzds2IzkjkbFnjuLQU4bVqOyqaRqHHDuInRt288oN74b1ivQ7rBeL/1gWUsZ8T+KT4/n6uR945+5PKS2sSnVt1jaDa1++mCFHDYj+4YB1izbWywg5/bbjueBBK4jy00e/QdO1kEag3WmvVI+12XXGnjWKy546d581QsCvcuocinDWTKmWzrHgHAPuP6jtitPAcQjETbR0QDI+QRbcCZ6Z1do6wHUqIvmW2B9XxB0FnnlQ9ik1C8jpgLRqwSgjRLEfoQwRRaMwb8qCsBthtEgJlz55HmPPHBm27fBJg3n5+ndCtmnRsTmXP30eF/WOLvX2uctfY9ua2sGtOdtyueu4R7nr0+vZtSmLbWt2kJASz+hTh9P1oE5hx63PeyUErFmwAYDyUjdLZ4bWntBtOmPPGMH48w7D8Bl07teB5PSGKbbWFBBCg9TnkMUvQukHIIv8NxIg/gxE4jWVMuxCb4lIfwPp2wy+fwE7OAYhtNjrh1hrE5B8HziGIUvfA+8yEHbL+5JwvtLjUOx3KENE0Sh43V4rcDRGFeI1XeOAgzsz6uTIxMNadGjO+HNH8+t704N6RUoKSvn44a858qKx/PDG1IjXEsgIAf+xAHDfKU8ihLDEx6Tk08cmM+yYgdz+8bXExTuDjtupT/uolVCr5gZ3mRsAwxu+v0Ri+qvr/lcQwoFIug6ZeDn4Vllvmr170BovwtYebO0baW0CXEciXEc2ynwKxd5EZc0oGoUDDu6M4Y2NFaLpGoeecgiP/npXLenxUFzz8v847LThlWNoek2XenFeCX98PIsf3pjKuHMOJTEtof6LrdChMiWG16g88pn3wwIeO++FkF3jk1z0H1O3b7+aTaPbwZ2tcZLjyewQOvvDNEwO8Lf/ryGEE2Hvi3D027uF5hSK/yjKEFE0CkOOOpiM1mlR6VfsSbeBXThwcFdOvulYLnvqXBKS46Pq73Daue2Da3jj36c5+n/jMI3anpGKjJRf35vO4IkH1Xmt4TANk5lfzGXzSkvwzOP2snj6v8z/aSG7t2THZPyj/NkuQghOuPrIoLEMQkCcy8m4s0fVe16FQqGIFnU0o2gUdJvOXZ/dwC3j78fn8dYIBhWaQEBYbYu1C9cjTcmqv9fx5ZPfMe7ZY8gY35YMZxKjW/Qk0RbZt9kOPdpa0u1h+P3jWRGNV1c0XePPr+cxW9P47LFvKMqzAlOFEAw56mCuevEicrbnRj2maZhc9tR5tOvepvL6cVdOZOEfy5g35R8EovLYSLdpgOCOT64jISUGHiCFQqGIEpW+q2hUtqzaxudPfMvvH83CXeahedsMjrlsAgeP68N1o+7GWx6ZUJME0KDs6Q54D3Dg1Oxc0W08p3UcHlH/oxLOjLiibkMSLIBX0zXSW6bS9sDWLJm2PKJsFyGg/5g+nHzjsQya0L9WO8Nn8NNbv/PNCz+yeflW7E47I04cwknXH0PX/uGDZxUKhSJSotm/lSGi2CtIKTFNs4bORnF+CW/f+TF/fPInZcXlOF2OkFVvpQ7e4YmU3dqq8tqtPY/jhPZDAKtg2+q/12EaJl0P7lSZBVJSUMKktPMa5sFiiKZrDJp4EPOm/BO0jRCCS544h2OvmICmaxHrlkgplUqmQqFoMJQhoog5xb5yynwe0hwJ2LTGKK8O9570BH9+81dI7Q/pFBR+3bXydao9nq8PuYG3bvqIKa//WhUgK6DPiAO5+8ubEMBJmRc28OpjQ3ySiw692rJq/rpaXhHNptGqUwtenP9I1PEyCoVC0ZAoQTNFzFiSt4k31v7OvJw1SCBed3Bc20Fc0OUwUhwNu/l5yj3hZdl9Ne/ne0u5+pKH2fzBHroZEpbOXMk5na/gvXXPk9Yylbyd+fVaX6x1UQJRWlTGXZ/dwOs3v8+0z2bXeD8OHtuHm9+5UhkhCoVin0Z5RBRBmbF7BTcv+AAAs5r6pIagTXw6bw69jNQGNEbeuesTPn7k66CbvRRgdHFS8lxNbYekU9ehFQU3EPqN7sXgIw7i9Vs+qPPamrfN4IABnfnrx4X4PNHpfPj6uXAfm4rRIw4MsM0vwfltPvrG2jErjjg73xa9j67rZG/LYcn05ZimpMfQA2jTtVWA0RUKhWLvE83+rdJ3FQEpNzz835LPLKGrPSSwTSTby/J4dc2vDbqGIy4aSyg7WUjwHJcKxQaOb/JwPbKD+Lu3hTRCAJZMX86ECw4jtUXdlTFLCks57/7TuPmdK6PqV352OiUPt8U3KAGZakNm2PAenkzx8+3xDk+s0Va3aYw9c2Rl3EezNhmMOWMkh581ShkhCoViv6FBDJGNGzdy4YUX0qlTJ1wuF126dOGee+7B49n7WQqKyJi6cxnFPnfQgriGNPl+2wLKfA3zM10wdSk3HX5vwKMZKaysGc/oJGSKRvI5G4h7PRv7zGJs/wQPbq3sLyVrl2wku5dJyfUtKLk2E/eJqZipkce+lJe4uWfSYww7dkDEwmfegfG4T8+wXtiqBYraBAgovbklZoZ1WqrpGs54J6fdenzEa1IoFIp9kQaJEVm5ciWmafLqq6/StWtXli1bxsUXX0xJSQlPPPFEQ0ypiDEbindjExo+Gdy74Da97CrPp2NiZkznXjpzBbcf8UBQXRGZpuM+pxm+A50kXrUFfBIR5QHjtf/f3n3HR1Wljx//3DszmUnvhfQQSui9FwFRQBR7RUXF3lfXtkV/u2v/6rprd22o2LAgdkG69N5bQnovpCeTmbn390eSMSFT7qQYIOf9evHSzNw599wEPU/Oec5zdn6A+c9RTQ2qWIH6BWGY/leM8fsKt59XbAr5xwvZs+Ygd/7nRp5f4LpKKkDDvCCwqaBzsFtFlkBVaZgdgOnjMuJSYnhs8b1EJ0d59mCCIAinmS4JRGbPns3s2bPtX/fu3ZsjR47wxhtviEDkNOGt82qzJOPIU/uXcmXiRKZHDkInuZ5gsyhWTjTU4Ks34at3fsbK/x76EEVRnSaqGutk4s9LIe3l3WDzPAhRDWBOblEavnkbqx7q74xAPmHDsKHabTs6vY6DG48wbPpgTfe1DvJ2HITYG5SIuiSZf9z5AAPG9xPbawVB6BH+sF0zFRUVhISEuLzGbDZjNpvtX1dWVnZ1twQnpkUO5K3UX91et688iz27M5kU3p/nR8zHILf9K1Vmrua9tNV8l7uDOlsDEhITw/txc/IMBgXFtbo2NzWfw1tTXd7TUmfB9EgWXnsqkDzctKICWEGqsKGaHAROikr9NSHoN1TjPgxQkWQJnU7jCqe7HUBAZGwoA8f019aeIAjCGeAPSVZNTU3llVde4bbbbnN53TPPPENgYKD9T1xcnMvrha6T7B/F1IgByG6G4+ZZk43FR3kndVWb90vMVdyw6XW+yt5Mna0xn0RFZVPJUW7e8habio+2ul7LllpJlkjdle5xEAI0Po0EXj87WX6RJZQkI2qE+xjdZlUYcfYQ+o1JxuTnvrx8VJHJ5ayRhMTY0L5u2xEEQTiTeBSIPProo0iS5PLP4cOHW30mNzeX2bNnc/nll3PLLbe4bP+xxx6joqLC/ic7O9vzJxI6zT+HXsGEsH4AbgMSFZUvsjZjtrUu0f7qkZ8pNldiO2n3i6I2Hjv/xN4lWJTft7+GRrueNYPGk2w7smwhKaDfV+f6GpPrxFWdXqb3sASGTh2It6+JC++c5bJPCYPi+NeVN2JzkXMjSxIDA2Ocvi8IgnAm8qiOSHFxMaWlpS6v6d27N15eXgDk5eUxbdo0xo8fz6JFi5BlzyZgRB2RU8Ohilz+c/gHdp3IcHvthxPuIqVpMK2y1HHuqqdcDr4AE8P6MSIkidnRw4k0BXL/lL9xaPOxLisWpgK2QSZq/s/xjJvOKnH9xv7MvGwy3725nJ/eWYlOL2OzKkiShIpKZEI4L67+B5EJ4QBYLVaevOolNizdar8Wb5mG0d6Y5kQx/OzBjI5IxobKS4e+d5h9IyOhk2ReGrWAsWF9HFwhCIJwejglSrzn5uYyffp0Ro0axeLFizWfgdGSCES6hqqq7C3PorC+nCAvX0YGJ7kt2/7B8bW8cXS52wTWjybeTf+AaAC2lBzjnu3ve9S3c6KGcnXtcB6a8Q9sVpvDhNWAUH8qS6s8arclVQLz/BDM14S2eU8CLokbxyODLmy8VlXZs+YAP7y9gqxDufgF+TLj6snMmD8Fb9/WyzGqqrJr5T5+fHclqQ2FHL1OxmpsnOmAxlkjH72RgQExbCs77rBvEhIBBm9+nP6ow3wbQRCE00G3l3jPzc1l2rRpJCQk8MILL1BcXGx/LypKbEfsTpuKj/J/B78lp+734+VDvfy4N2UOc6JHOP3cyJAkt0GIv95EUoutvL/m7/O4fysK9rLfO4t//Pwo/7vnAzIP/L48FxwZyE1PXUP+8UI+e+6b9s2YSIBBomG242JmvUzB3Nb3nN8vlySGTx/McA07YyRJYuTMocRPTuSy9S+h2BrgpIJwtdYGp0EINAYrFZZa/nv4J4KNvgTovZkeOYgwkwjEBUE4M3VJILJixQpSU1NJTU0lNja21XuncEX5M97W0lT+tOODNuFEaUM1T+z9ApuicH7sKIefHRwYx4CAGI5W5TtcapGAKxMm4tXit/g1hQfb1c/8unI+CdvF23tf5NjO4xSkFxEQ6s+QKQPQ6XWU5JXx3Ru/UFNZ1yYYkXQS4TGhXP7nC3j9vkUgg2prfGJVBvQS+n8lo4a0zueQgLnRI7l/wFwCDN7t6nezpTnbqLc1OAzcVA1bogGWZG1CJ8koqsqLh77n8vgJ3Jcy5w87cFAQBOGPIs6a6SFUVeWqDf8lo7rI6VAYoPfmxxmPtQomWiqoK+e2rf+joK7c3oZOkrCpKlMjBvDs8GvsA6Wqqoz75a8d6vOSyfc7LZaWtieDv897luLsUnQGHahgs9pIGBjLv757lPCEMG7/4lWOfbof3b5aVJ2EdbQvDecFIIV50S+gF9MiBtKg2OjjH8XUiBS8dAaH9/LU/A2vcKwqv1PaaiYBl8dP4M8DL+jUdgVBELpCty/NCKeeY1UFpFcXubym0lrHxuKjTIsc6PD9KO8gPpl0Hz/k7uSnvF1UWGqJ8wnj4rixTI1IQW6xNVWSJIK9fDnRUNOu/krAhuKjJPpFUGs1szR7K19nb6WovpJALx/mxYziv4ee49iKoxzYeARZlhhx9hBGnD0ESZJYnr+HvYGFcHt4m7ZVVA5X5jE/cQqzood53DdFVdhXnk1RfQWhRj+GBSe22pbboFhcfLp9VODLrM0s6H0W4WKZRhCEM4gIRHqIUrO25M4Ss+sicr56I1ckTOCKhAlu27owdgwfHl+rqULrySQkLIqVioZabtv6P9Kri+3LGkX1FbyTtopFx9cwOS6Fax6dzLCghFbbZ7/J3oaM5PTeMhLf5GzzOBD5regwLxz6jry6E/bXIowB3D9gLjOjhgAwMDCWnNoyp7uFXPXLFRVYVbCfKxMnevxZQRCEU5U4fbeHCDX6ub8ICDX6d9o9r06cSJgxwG3pd0cUVPoHRPPi4e/JrClxmFthVRXWFB3k1i3/49kD36CoCqqqsq88i2NV+S4HewWVvNoyp+87sqH4CA/u/Ij8FkEIQJG5kr/s/pQV+XsBuCx+vMsty+0JQqBxGazK6rr+iSAIwulGBCI9RF//XiT5RiC5KEzmrzcxKbzzyosHe/nx7vjbGB2S3Op1g6RzWR5NRqKXdxD9AnqxIn+v2zok0Jgg+ubRFVy78RUWbn6TCoubgmVAsMbgDBpzXl48+B2u0k3/feh7rIqNIUHx3Jw8w/4szZr/PSUgpl3BmVVViPFxX/BNEAThdCKWZnoISZJ4YMBc7tu+CMDhYHpfynlOE1XbK9I7iFfG3EhubRmHK3PxkvWMCEkivbqIe7e/T43V3Op6GQmTzsCzw+eTWlWgKQhp9kH6Ws1zDSpwfsxIzW3vr8huteXZkdKGaraVpjEhvB+39p1Jv4BoPk5fz97yTFQal2yuSZrMDzk7OOzBczXz0RmZFjGQ9UWH+SprM2nVhfjojJzTawgXx43t1NksQRCEP4oIRHqQcWF9eWnUAp47uKxVjkOwly/39p/DXA8GZk9k1pSwumA/tTYzCb7h6CWZIUHxfD/tUT5KX8ey7G2UNlRjkHT08g5iQGAsVZY6t2XlT6Y1CNFJMjHeIZznom7KyUo059j8ft20yIFMixxoD6aaZ0HWFx1qV57IgwPm8tzBb/kxbxeyJKE0bXh7J3UVn2Vu5PUxN9MvoJdHbQqCIHQ3EYicJiyKlVUFB/gxbydlDTVEewdzUewYxoX1abVbxZ0J4f34euqD7C3PoqCunGAvX0aF9HZZnyK3tow1hQeosZqJ9QkBJNYVHaLGWk+iXzgXxY4h2b91oTqrYmNN4QEWHV/L0ap8ZCRkScKqKrxw8Dv+NuQSzo4awu19zyHcGMALh77Dpirk1JaRW3eCX/L3kBIQg0k2UN/Ju1CGByfyr2FX4qM3av5MqJe22YYwB7MSLZdhCuvKOVSR2648kUXH15Jd23jEgtJi172CSrWlnj/t+IBlZz0kao0IgnBaEXVETgMVDbXcvf09jlTm2X+T1kkyNlVhWsRAnh5+dZcMPg2Klaf3L238Dbwpu8TWNIBKNJ650tyPhcnTubXPTCRJ4reiw/xr/1dOt+5KTZ9/dcxN1NksPLjzQ4fX6SSZUC8/itzs5NHKT2/k7XG3k+wf6fFnFVXh0nUvkld3wmkIEezlyw/THnX6s6iy1DF/wytNhwB2zTk6zw6/hhlR7qvACoIgdCVPxm+RrHoaeGLvF/YCWc2/STcPZGuLDvLWsV+75L7/2vcVP+Xttt/X1mIIVk/qx7tpq/kxbxc7y47z550fuawf0tzK26kreS9tldMlGJuqUGSuZHRI744/DGBVVJ7a/zULN7/J26krKa7XHuDIkswDA853ec2fUua6DAi/ydlGYX1FlwUheklm14n0LmlbEAShq4hA5BSXWVPCxpIjrabiW1JpLAdeZ23o1PtmVBfxS/4ezSXJJRoPxnvtyHJN1yuo7DqRwYGKHJfLFDpJZkBADG+OvZnJ4Sltdps0BzFa8knqlQb2V2SzrzyLd1NXcfG6F9hUfFRTfwGmRAzguRHXEmlqfU5NqJcf/xx6BbOjh7v8/Pc5OzR/P9vL1a4oQRCEU5HIETnFbS9Nc3tNna2BQ5W5jAxJ6rT7/lqwz16+XQsVyKgpdnudpyTArFgZGdKbkSG9Ka6v5Iuszfyct5tqaz1xPqFcGj+Oo5V5LMnarLldBRWLYuWhXYv5asoDRHoHafrctMiBTIlIYVdZOsXmSkK9/BkZ4v70YoByS63m/rWHVVUY2UmzR4IgCH8UEYic4hRVQcL9jhClk6f7qyz1SMiArVPbbclL0mHUGaiy1ju9xqoq9GmRCBtuCuDOfudyZ79zW11XXF/Jz/l7qLLUebSF16rY+Dp7K3ec1J4rOklmdGiy+wtP0ss7mPKGWqezIjLQ3p+iTpKIMAYyuRPrwAiCIPwRxNLMKW5ocILbgdUg6egXEN2p943zDcWmehaEBBi8NS8M6CSZubGjuDR+nNNlFQnw1nlxbq+hbtsLNwXw9rjbSPSNaPqstkUKBZVNJcc09rpjLo4b63JpRksQMjgwDmi9E0dCIsDgw0ujF4gdM4IgnHbEjMgpaldZOp9lbmRH2XH7zhRHZCTOixnR4aPrW6q01DEutC86ScaqcaZFQuKK+AmkVReytvCgm7wPiXBjALf2ORtvnRdbSlI5UpnX6jPNA+0/h16heZttkl8En02+j90nMjhYkYNe0vFu2iq3SyKeBlztNSd6ON/mbONAueu8mJM1//znxYzisUEXseNEOl9lbSGtqhAffWNBs3kxown08unC3guCIHQNEYicIqyKjVWF+1mWs51jlQWUW2rsW2QdaV6uSQmM4f6UuR2+v6qq/JK/h4/S19t36AQafKjQmNcQZPBheHAiuXVlbpNP50aP4I5+59orgb459hY+yfiNL7M2U9pQjYzE5PD+LOg9jcFBcR49hyRJjAhJYkRTvszu8gxWFex32iMJqVNzawD2lWfxddZW0qsL8dGbOKfXEGb3Go633otXRt/EK0d+4rvcnTQoVk3tDQqM5c5+sxgRnIgkSYwN7cPY0D6d2mdBEITuIuqInALqrA3ct2MRu09kuAw+mvnqjcR6h3Bx3FjmxozEqDN4fE+rYqPO1oC3zgu9rOPNYyt4L211q/tryU1p1nyts4qhwQZfru89lYvixuLrZIZDVVVqbQ0YZX2nLTG8fuQXFqWvdXnNG2NuZlRox5M8VVXlP4d/5NPMDfZZjObvS5QpiDfG3mw/K6bKUsfRynxePfozBytynH6fZSSGBifwv3G3drh/giAIfxRPxm8xI3IK+Pfh79l7IhNwdaRaI50kMzNqCH8dfEm77lVQV86i42v4IXcXZsWCUdYzIawfa4oOtrm/JxFq87WOghAJCZ0sc2XCRJcBhiRJToOU9tpY4np7rgTsKDtuD0RUVcWsWDHKeiSpMcvEbLOwqnA/W0vSUFAYEhTP7Ojh+OlNrdpalrOdTzM3AL/XV2n+bhSbK/nTjg/4bPJ9yJKMv8GbUaG9qbbWu/w+K6jkenhKsCAIwulEBCLdrLyhlh9yd2rOGbCpCrvKMtp1r8yaEm7e/CbV1nr7QGlWrKxtCkK6iopKibmKTSVHmRIxoEvv1eq+qkpqVYHra4AjlXnk1pbx4fG1/Ji3C7NiJcDgzUWxY5gU3p+/7vmMEnMVOklGVVV+ztvNq0d+4fmR8+1LJKqq8mH6Oqf3sakKGTXFbC1NY3xYX/vrQQZfsih1uQQXJHI/BEE4g4ldM93sQEW25oTQZu05Qh7gyX1fUW2ta5P4+keszekkmePVRX/AnX4nSY3n27giI1Fvs3Dthlf4NncH5qa8jUpLHR+lr+OOre9QZq4GGoMJpSlkqLM18MCOD8muaTz7pchcSU7TOTDO6CSZraWprV6bGzPS7SxYVx1GKAiCcCoQgUg38zRFR5YkJrWjVkR6dRF7yjM1FyjrbIqqYnKQy9KgWDu9Bkqz7JpSt+XUFVTSa4qoszU4DNAUVIezVSoqNlVhSdbGxnY0PIOqqlhsrRNUZ0cPI9pFMbUIUyDzYka7bVsQBOF0JZZmutmgoFiX23NbkgAdMpfGj/P4Pu6WKP4IU5uWZeqsDXyWuZEvszZTbK5EL8lMjxzMgt5ndeox9l9mbXab/GuUDZSYq9rVvk1VWFWwnwcHXECEKZBQLz9KG6qdXq+gsr74MLdaZuLftN260lJHtdXs9DNRpqBOz5sRBEE4lYgZkQ6qsZpZnr+HL7M2s7H4CFbFs5oUwV5+zO41zO1ZKTISelnHcyPm23deeMKk8/L4M8337SgZiTnRwwkz+rOp+CjXbnyFN48tp7jpVF2rqrCqcD83bnqdLRqLi9lUhfVFh3lo50dcu+EV7t++iBX5e1t9/zcUH3Gbe2PS6Tv0hM1LOTpJ5qrESW7byq87wfMHv7U/w5P7vqbSUuf0+j3lmWwvO96BHgqCIJzaxIxIOzUnJ76buop6xWJ/Pczoz2ODLmZKRIrmth4ceAHp1UUcrMy1/wbfvO3TKBvo4xfJhPB+XBQ3hoiTDlzTalRIEibZ0KqvzjTnoNhUBaPOQJ1N24F6KQHRHK1srEHSvOPEpipMjRhAkm8E561+1mldksb8C4m/7P6UH6c/5nJLstlm4aGdi9lcegxZklCaklI3lhxlUGAsr4y+CT+DSdMsU6UHJeFPJiPRz//3GZz5iZP5regwe8oznX5GBVbk7+Xu/rP5z+Ef2FzqOvDSSTI/5e1iTDtKyguCIJwORCDSTu8fX8Obx1a0eb3UXMVDOz/i5dE3MjZMW9EpP72J/42/jV/y9jQeFV9XTqjRnwtiRzE3eiTe+vbNZrTkozdyTdJk3ktb7fB9CbguaSqDg+LZVpqKVW3cprr/RBbf5m53m1D7v3G3MDw4iaL6Cn7M3UVuXRkBBh9m9RrKivx9vHbsF7d9VFGpstazqnA/c6JHOL3ulSM/s6Up6bP5VOLmmY9DFbk8deBrnhl+DcOCEyioL3cZkHQkY0ZB5fKECfav9bKOeg1Bm4LKorQ1rCzY7/Zam6rYk2UFQRDORCIQcSGv9gRHq/LxknUMD060lxqvtNTxbtoqh59pHthePfozH4bd3fo9tTHx0dGuFy9ZzwWxo7ggdlSnPoOqqmTUFFNlqeOi2DGUN9TwdfbWVn2wqQrzYkdzR79z0Uky0yIH2t87VJGj6T4JvuFAY3LlDcnT7K/n1pbxoZuCYi3pJZljlQXMcXJ0TpWljm9ytjnN+1BQWVmwnyWZm7gkbiw/5u3SdF/Pirc1zlrNixnFtIjfv1fF9ZUcaapK685vxYc13VMnyURpPBlYEAThdCQCEQcK6yt4ev9SNrUohmXSGbgqYRK39jmbNYUHsLjIBVFROVyZR2ZNCQm+Yew+kcHi9PVsKD6CTVXo7RfBVQmTuCB2VLu24paZq1mWs52NTe0NC07gkrhxxPmGtrpuXdEhXjvyC+k1jdtmJWBCWD9eGnk9u05kUGyuIszoz3nRI0j2j3R4r+HBiXyRtdllf+J8Qgky+Dp874fcnZqqxTZTAS+dnmNV+awrPES9zUKyfyTTIwdh1BnYX56tqTT6C4e+I8TLj6sTJvJp5kaX10pAqNFfc9Jqkl84VydMordfJC8d/oEKSx0x3sEkNgVjWhTWV2i6rjlIFARBOFOJQOQkZeZqFm56k9KG1oNSvc3CB8fXUFRfQZJfhKadLuUNNewvz+Kf+75EbnF9enURTx9YypbSYzw57CqPgpGdZen8accH1Nss9sH9UGUOn2Zs4G+DL+H8phmVn/N28/jeJa2SJ1VgS2kqe8uzeH/8HST4uR84p0UOJNzoT6m52mni5/zEyfackJMV1lc0vqdx27BNVdhWksZ7aauRJQkZCauq4K838eSwqzTlfTQrb6jh29wdmq4dFBhLg2Jjc8lRlyGTDpn3x9/BX3Z/xlMHltp/dqqqag62hgTGc6AiW1MRuz5+UQwMjNXUriAIwulI7Jo5yScZv1FirnQ44KnAj3m7sCo2TQOiTpJ4cv/XqNDq+ubhZ2XBfr7L0TZQApxoqG4ThADYmpZ8/rX/aw5W5FBva+C5A8ta3ev3axXqrGb+e+QnTfc0yHpeGnWD0xNwL4wZxcVxY51+3ldv9KhOiJ/eyIGKbKAx/6M5N6XaauaBnR+ik2TNgZuCSp21gRAvPzdXSgwJSuDS+HGugxBJ5uxeg3ly/9f22TKbqrQqdKbFP4Zezviwvpr+48usKabaUq+xZUEQhNOPCEROsixnu9vTY4vqK/DROa/tICMxOqQ3m0uOuSxYJiGxxM2yQUvf5uxoE4S0uq8k8UnGb6zI30uNzXltChsqG4qPaF6KMCsWbKrN4dbUEnM1T+z9god2LuaVIz+TVVNif8+q2NhemuZRQmi11ey0gBjA0uytnBM1RPO2YgWV8oYal9foJZl5saOYFN6fPn6RDgMdqenPrF7DWFGwT3NJ/pPbuCh2DLG+oVyXNBUt4ZlFtbVaIhQEQTjTiECkBZuquD323qYqlDXU8OCA8x2+LyNhkPXcl3IeRyrzXQ5YKipp1YWaZww2lxxz+Xu3TVXYXHKMjzN+c9uWikp+7Qn75/aXZ7O55FibA9bqbQ08sOMDzDarwztvKDnCL/l7WFt0kI/T13HZ+n/zXmrjzpz1RYdIrS7U9Gxa2FSFdUWHuLf/eST7R2qu/+EuaBgaFM/je5bwyK6POS9mJMl+jfkyOklG3xSU+OiNvDDyOnJry5A03FmiqfZLixmcWdHDeWjgBQCMCu3N5fETXLTwu1qN26cFQRBORyJHpAWdJOOnN1FtdT4VrpNkQo1+XBA7CpPOwKtHfya/rtz+/qCgWB4aMI/+AdF4yTpkJDczLDpNAxugaTnIolg1n+ny5rEVzIoextupK1slT44KSeKhgfPo7RfJ8vy9VLgouAW/z1Y09+7N1BVUWevIri11+/yeUlAxyDreHX873+fu5PWjy13+vLTYcSIdaJyhWld0iDCjP48PvpS06kIaFCv9AqI5N2oo3novjlbmI0uS21L5L4y8jtSqQgrqThDo5cusXsPaJARfkTCBL7I2ue1fkgdJsIIgCKcbEYic5IKYUSzJ2uh0oLGpCudFNx5Cdk6voZwdNZjDlXlUNNQS7R3cKgF0UngKKwr2Ob2XTpKZHN7faaLnyYYHJ7D3RKbTgV2HRJgxgNzaMk2D/7ayNLaVpbV5fVdZBgs3vcn7E+5g94mMdgUTH2f8hl6SOzUIgcZZhhJzFcn+kVwWP57efpHcvvXtTmm7OaA60VDNG8dW8PXUB9sUVkv2j3IbEOolmaFBCW5PGk7wDWNEcCJ7yrMczorJSMT7hjEkKN7DJxEEQTh9iKWZFiyKlREhiXjrjA7nKCQkpkUMZEhQnP01WZIZGBjLhPB+bXahnB01mEhToNPkSlVVuTZpqub+jQ/r63JYt6HSxz/K7Ymz7iio1CsWXjv6i+bZGkc8PVVYCxWV27b+j8KmWagRwYlcGT8RoFVPm//dIOk8vodNVSk2V7KqsLHgmKqqHKrIZUX+XmRJIszL32mOik6SObfXUAK9fDTd67FBF+Gr82rzd0QnyRhkPU8MuUxzoCoIgnA6EjMiNA40izPW8+HxdS5zRHr7RfDPYVdoHhiMOgOvjlnIPdveo6C+HNlevr3xePrHh1zG0GD3v+02KFae3r+UH/N2ORz+mrcSL0yeTrgxgDWFBzT1z5Xms1weHHA+32ncAvtHUGk832dxxnoeHHABkiTxwIC59A2IYnH6ejJqigGI8Q7hmqTJ5NWe4OOM9R7Py8hIbCw+SqxPKE/vX0pai1wXX50RSZKQ1db5J7IkEWUK4t7+52m+T6JfBB9MvIv/HfuVFQX7sKkKMhJjQ/swKbwflZY6aqxmhwffKapCYX0FiqoSaQpEL3sedAmCIHQ3SfX0HPo/UGVlJYGBgVRUVBAQENBl9/n3oR/4LHODpmsjjAGEGv0ZHZrMJXFjNR1A16BYWVWwn9+KD2NRbKQExHBB7CjCjP6a7vnEniX8nL/HaaLq4MA4bu97DmPD+lBtrWfOqmcwazhTRov3x9/BLVve6pLZjY7w1RlZfc4TrV5TVZUKSy0qKkEGXyRJoqS+kkvX/1vzeTnNJCRGhfRmb3kmVsXmcImpr38U6dVFWFUFP72Ji+LGcH3SWQRpnA05Wa3VTEZ1Me+krWRD8VH7z9soG7g8YTx39j0XvaxDVVW+zt7KR+nryKtrTDgO9vLlioQJLEg6SwQkgiB0O0/G7x4/I3K8ulBzEAJQZK6kyFzJkco8Psn4jSeHXcnZUUNcfsZL1jM7ejizo4d73L/MmhJ+yt/t9H0ZCVmS7efa+OlNPDboIv7fvi88KlvuTIQpkDGhyWzSeCruH6XGZsaq2FoNupIkEeTVusJrmCmAfv69XB5E50xBXbnTIAQgo6aY76c9ikHW4as3Iruob2JVbPxasI9lOdspqCsn1OjH+TGjmB09HFNTHopNVfjb3s/JrzvRKug0KxY+Tv+N/LoTPD3sal489D1Lsja1mh070VDD/46t5EB5Ds+PmC+CEUEQThs9Pkfk+5yd7SqzrqCiqAp/2/M5mdXFXdCzRisL9rmsmaGgsrc8k5L6Svtr58WM4D+jFjCgAxU5ZSRGBicRbgrg6sTJ7W6nqwQZfDQPtu3JmdEhkVNX6jLZ1qrYWFt0EH+Dt8sgpM7awB1b3+HxvUvYVZZObl0Z+8qzefrAUm7c9Lq9zsmSrE3k1ZY5KabXeIbOV1lbWNK00+bknqmo/FZ8mBUFez1+XkEQhO7S4wORgvpy+wmunmr+1BINWzDbq9par2kgrbG2LmA2Mbw/jwy8EEMHfsTNh9eNDU1mfGjfDqStdi4ZiYtcVHM92ZCgeHQeBiNhJvdLgTpJprhFAOjMS4d/YF95FvB7TknzjEdGTTH/3PclAN9kb3NbTO+TjN9cBs4yEl9mbXHbJ0EQhFNFjw9Egrx8OrTLxKYqXVr5Mt4nzO12UYOkczhwvnjoO2ztXJxRUPnr7k85UJ6NhES4yb+TN+K2jwT4G7y51INA5OK4sVqPurErrC93e41NVdzm+VQ01PJ9zg6nAYZNVfit+Ag5taWUmqvd3q+socbl3wcFlcyarpuhEwRB6Gw9PhCZ3Wu4RwepOdLeGRUtZvYagpdscPq+TpKZETWYaksdZtvvCapZNSXsLc/qUB2PGquZe7a/z6cZG/gud2e72+lMKlBhqeWq3/7D2sKDbd5XVKXN6bwxPiH8ZfDFAK1mE1wteWn5ruklXav8IFVVWZG/l4Wb32DiL39j8vLHuXHT61g1FHPfXZbhNslVJ8l46wxut1S7On5AEAThVNPjA5EhQfFMCU/RfHbJyXSSzMiQpE7u1e/89CYeHXQhQJsBSEZCh8Ty/D1csPZ5Zq58kucOLKPEXEVBi2qv7aWgUm2tZ9HxNR1uq7PV2Bp4eNdi9pxoTEI9UJ7NwzsXM3H535m8/HEuXPs8i9PX24OSebGjeWfcbUyNGIBJNmCQdYQZndcDacnZFbf2nWmvF6KqKs8eXMZf93zGgfIcrE0BUU5dmZNPtzU3eqTL/thUhXN7DXNZ5l9GYnb0MM33FARB6G49PhCRJImnhl/NeTEj7IOAJyGJTVU0nxnSXnNjRvLvkdfTz7+X/bXGmiSNRcOahyWzYuGbnG0s2Phah2d5Wip3c/5Od1GB14/8wurCAyzc/Cbriw/bZ6fy68p55cjP3LvtfftM0dDgBJ4bMZ915/6DDef+i3FhfTXVhHE07J8dOZjrWxSjW1W4n6XZWwH3Z9s4src8iy+zNjv9rIzEuNA+3NrnbHp5BzvME5ElCR+9kcvix3t8f0EQhO7S4+qIKKrClpJUdpQdR6WxbPrE8P72U3U3lRyjwWahb0AvBgXEUq9YWJ6/l+cPLkNuKhwGvxcR+/OAC7gioW0gklZVwNLsbRyvLsRXb2RG1GDOjhqCl9yxHdP5dSeoaKjl3u2L7DUzTqaTZGZEDuJwZR7ZtaUdut/pwEvSY1EdH8onAZPDBzAmtDcpgTEMC0qwBx+vH13OR+nr2h20vT3uNoYFJwBw65b/uSy/74qPzot6m8VlEHJezAgeHjgPk86LvNoTPLjzQ9KqC9FJjeGzVVWIMAbywshrSQmMadfzCIIgdBZPxu8eFYhk15TywI4PyKwtafU/8F6mIF4cdT19/KOcfvZgRQ6fZ25ka0kqKjA6tDdXJkx0eA7I26kreTt1pT1YkZoqqsb5hPL6mIVEegd16DnWFB7k4V2LXV6jk2TmJ07mw/R1HboXgL/eRFUHD5brbs0/gyTfCJ4efjXJ/pFkVhdz+W8vtas9nSQzNWIAz42YD8CU5U+0q4icl6x3WatEJ8l8Ouk+Ek86PkBVVbaXHWdrSSo2FIYGJTA5vL+oHyIIwilBBCIOVFvqufK3lxzuOpCR8DOY+Hzy/YRqrHbqzM95u3l87xKH7+kkmSTfcD6edG+Hzg9ZlLaGt1J/dfubfPMdOvoDvjhuDEuzt3Wwla7jyaF8OknCV2/i44n3EOkdxHMHvuGrpiUVT/nojKxpqu46/dd/tNlCrYVR1mM+Kbm2JQl4IOV8rkyc2K4+CoIgdAdPxu8ekyPyfe4OSsxVDgdvBZVqSz3fdHCwVVWVD46vdbqrwaYqpFYXsrnkGKsL9vOnHR8wf8PL/Gn7IlYW7MOq2DTdx6QzODyttU1/6HgQAjDAP4Ywr44FaF3FKBs8CupsqkqN1cwnGY3VdP88cB43JU/HdNLOJF8NO0+UFrthpoSneFQYT6JxSclVEAKNwevxmiLN7QqCIJxuekyJ9xX5e10OygoqP+fvZmGfGe2+R1lDdavD0RyRkXhq/9cUmSvtv8mnVRWyoeQow4MS+M/oG/BxcMBZS9k1pX9oTQ+rYqOkoeoPvKN282JH8UXWZo8+Y1MVfsjbyZ8GzEUnydze9xyuS5rKjrLj1Nsa6O0XyaqC/byXttrlksnwoET719ckTmZ5vraKps1J0Y8MupB/7f/K5bUqjTkkgiAIZ6oeMyNSrWHavD1T6y1pSXpUUSkyN1bjbB7kmv+5uzyTP+/8yOXnU6sK+CLbs4G3I3RI7S6K1tXGhCRzX/859PPv5XGZ/ipL65wXX72RqREDOLfXMPr4R3FR3BhkyflmWpuqcFWL5ZKUwBj+NexK9JKuxe6rxn8aZT1+elPTazA2tA9vjL2ZC2JHMTqkt9stu9MjB3n0bIIgCKeTHjMj0tsvgqzaEqfBgoxEkl9Eh+4RavQnxMuPsgbnFTLdDenby47zwfG1LOh9lsP3l2ZvtSfBOtOZyaVzY0d1KJ+lK8hI3NL3bPtJs/8ZtYB7tr9v30WiJSCMcFPCPcIUyJPDruSvez4DaLNbamHydCaG92/1mXN6DWV4cCLf5mxnf0U2BknH+LC+zI4ejlFnoMpSh0lnwNRihuOm5OnsKDvu+DklieFBiQ4TogVBEM4UPSYQuTh+LCsL9zt9X0HlsrhxHbqHTpK5ImECbx371eG2Wq2n4b5+9BfOix5BuIPBMrOm2O1AW2dtIMjg0+H6H4MD43hk4DyOVuZ3qJ3OJAGjQ3qzMPn3JbQwUwCLJ93DxuIjrCk8SFF9BVtKU122MzQwnrSqApJd7JSaETWYxb73sCRzI+uLD2NTFYYExXNFwgTGhvZx+JlwU4DT5b2TTwYGGB2azD+HXsm/9n+FRbGik2RUGgOf4cGJPD/i2lMuEBQEQehMPSYQGROSzLyYUXybu8Ph+1GmIHz1JlRV7dD/+K9LmsLuExlsLjnWKvBo3C4sYVW1JaR+m7u91WDbzFdvcrtLxMdg5O7+s3ly/9fteILGaq5/HnABMyIH8X7aGj7P3NiudrqCClwYN6bN6zpJZkrEAKZEDADg8nX/JrO2xGk7Kwr3saJwHwMDY/nr4Ivp26JYXEvJ/pE8NvhiHuuU3js2K3oYE8P78VPebtKri/DWeTE9ahCDA+NEECIIwhmvx+SISJLEXwZfzIMDzifSFNjm/aL6Cu7e/h6P7PoYi5udDK4YZD3/Hnk9fxl0MX38o/CS9QQYvLk4bgyvjrlJc9XW9GrHOyVmRg1xe0LrrF7DmBc7mscGXeTxuSMyEpfGjWN29DD+vncJ76atPqVqiCT5hrvNmSipr9RcyO1wRS43b36LDCff7z+Kv8GbKxIm8MigC7k3ZQ5DguJFECIIQo/QY2ZEAGRJ5sqEiRyqyOWnvF2thvPmwX1t0SFeO7qc+1POa/d99LKOi+LGcJGD39ynRgxkbVHbw9pO7qezAGJa5EASfcPJri11uEQjI3FRbOPJtBfHjWVO9AjWFx1iU8lRDlbkkF1TgsXN6a0Xxo1mfdFht/3sDjclz3BbtGtLaarmuiIKKg2KlbeO/cozI67BqthIrSrAotpI8o3Az2DqjG4LgiAITvSoQASgxFzFz3m7nQ5TKipfZW3h5j4z7DsdOtNfB1/MtrWp1NoanF7TeNx7NbdsfgujzsBZEQOYEzMCP70Jg6zn9TEL+dPODzlSmdfmsxbVxt/2fsZbY28hyMsXk87AOb2Gck6voaiqysXrXiTPzUFsqVUFfJW1xaNCYX+UwUFxbq+xaKzH0symKqwuPMB7qav4PGsTJxpqADDIOuZGj+Te/nOotzXwTc42dpalIyExOjSZebGjOlwATxAEoafrMZVVm7mqfNrSf0YtaLMrorPsKD3OndvedZLQ2liKvOXuD4nGRMfXxyy0J1c+vudzlufvdRgo6CSZCWH9+Peo61u9nl1TyqXrX+z8B/qDDAqI5fmR1xJm9G+zbFFpqWNHaRoW1YaXrOfhXR93yj1lJCK9AympbyyG1/z9lpDwkvW8MPJaxoX17ZR7CYIgnCk8Gb973IyI1gPOrJ14eu3JRoX25v9GXsvf93xOna0BfYudEs3BSct+qkBFQy33bH+fpVP/TLW1nhUF+5zOVthUhd+KD5NbW0aMT4j99YZ2nIVyKjlQmcPcNc8S4x3CtUlTuCRuLFbVxsuHf+brnC2tZkLcHSSnlYJKfl15m9fVpiWdB3d+xJdTHiCqg+cHCYIg9FQ9LhAZoOFkUgmJ/gHRXdqPqRED+HH6Y/ySt5vU6gK8ZAPbS9NIrSpwOHgqqJSYq1hVuB9fvUlTQLW3PLNVIBLtHYJRNrTrcLZTSV5dGc8dXMbRyjwqLXWsKtzf5jtWb7Ogon3LdHuoqFgVG19nb+HOfrM0fcamKmwpOcaxqgJMsoHJESmtfkaCIAg9TY8LRHr7RTIyOIk95ZkOB3OdJDMlPMXhzprO5qs3ckl8Y+0Sq2Jj4vK/u7xeliQ2l6QyM2qwpvZPPvPGW+/FvNhRfJW15Q/P/dBaaEyL5p4vzXF+NpCCiozEgIBYsmpLqbLWdcq9Hd3nt6IjmgKRvScy+euezyisr0AnySiqyouHv+fcqKH8bcglrQqdCYIg9BQ9ZvtuS/9v6OWEePm1Ka0tIxHtHcyjgy76w/vkKF/EwUXYVBtDguLdljSXkBgenNjm9Tv6nktvvwjN24jd0dLO9IiBTAnv77KUeVdQUMmpK+OrqQ906X201IZJqyrkrm3vUlzfWN6/5TLcrwX7eGz3p5zC6VqCIAhdpkcGIlHeQXw86R5uSp5OhCkQg6wj2juY2/uew6IJdxJi9PvD+2SQ9ST7RTo9uRcag5XBgXEEefkyN3qE04FdRmJ65CCHeQt+BhNvj7+dUSHJHe6zjESY0X0S8dqiQxyrKuiWuhgVllr89EYCDT5d0r5OkhkWlOD2ukXH12BtkezakoLKhuIjHKjI6YouCoIgnNJ63NJMsyAvX27tO5Nb+87s7q7YXZ04yWk1VAkw6gzMjRkJwIMDLiCzpoQ95Zn2bbbN/+wX0Iu/Dr7Y6X189Uby6060q4/NJdZvSJ5Okm84l//2ktvPKKjktvN+HRVk8EEv67ksfhzvpq3u9PZtqsLlCeNdXmNVbKws2OdyaUonyfySv1vT9mRBEIQzSY8NRDqbVbFRYanFW+eFj96zaqbNzo8Zya4TGfyQu7NVDQ+dJCMj8ezwa/A3eAON+R5vjL2Z1YUHWJaznYK6csJNAVwQM4qZvYbgJbv+0Z5wcTCfKxISwV5+jAltnFGxKl23u6ijJODC2Maicgt6T2NZznZKzFWdeo8HU86nn5vE5nqbxe0uLFVVqbR0TR6LIAjCqUwEIh1UZanj/eNr+CZ7G9XWeiRgXGhfFvaZwbBg91P2LcmSzOODL2VyeH+WZG7iSGU+XrKe6VGDuCphYpvTgfWyzl6s7GS5tWX8kr+H8oZaorwDmd1reKslp1CjP7Uay6C3pKAyMiTJ/nVKQDR7yzM7LfW1Ze6LTVXwkvVYFVu7kmt9dEauTpwEgElnYPHEe5i/4RVKG1oHI81Bn6c7bF4ccR1TIge474feCz+9iWo3pfJjvMXuGUEQep4eV9CsM1Va6rh585tk1ZS0GiibczeeGXGN23NR2sOq2Pg8cyNLs7dR2lBFkMGXuTEjuCRuHAEGb1489D1fZTdWRpUlCaXpIL/b+57Dgt5nAfBR+jpePfKLtiTZFmQkvjvrEY5W52NRbJSYK3n+4LcdfqZAgw+vjr6R5QX7KDNXE2EKYG7MSOptFh7Y8SFF5gqP2/xgwp0MCIxt9VqN1czi4+v4KnsL5ZZaZCSmRQ5kQ/ERzB6cMSQjsfac/4dRZ9B0/ctHfuKTjN9QnPznJgFLpz5EtE+w5j4IgiCcqjwZv0Ug0gEvHvqOL7I2Ox1cfHRGfpr+GN76ztuWuacsg7u3v+dw0Awy+DA5PIUf8nY6DS/+MuhiLoobQ7W1nhs3vU5ObZlH22p1SOhlXav7R5oCKax3HSjoJR2Kk2TNZv38e3FV4iTmRo9oldhqVWysLzrEO2mrSa3K1xQ63d73HG5Knu70fVVVqbU1YJT1rCk6yF92f6qh1UYyEmdFDuS5EfM1f6a8oZYbN71OQf0JbA7+vlwUO4Ze3sHYVBuDAuMYF9YH2c3OKEEQhFOVCET+AGabhXNXPUWdizNjAP4++FIuiB3VKfdcfHwdLx/9uUNthBn9+W7aI+gkmeyaEu7a9h4F9eUdalNGwqgzuPxePDTgAj5MX0dRfYXTQKJ5iWRezCj+OviSNrtsjlcXctVv/3XZF1+9kbv6zeLSuHHYVIX1RYfYX5GDTpIZH9aHEcFJrdqttNRx3upnaPBgNsQg6Xh3/O2kaCiO19Ky7G08c+CbNsGYSTZQr1iQkZAkCZuqEO0dzPMj5rvNPxEEQTgViRLvf4A9JzLdBiF6Scfx6sJOud+v+fs6HIRA46F/Byty6OMXxSO7PmnXksfJFFTMNgsTwvqxqyydesViPzMnwODNgwPOZ070COZEj+D73B0sy9lOmoPvS/MA/W3uDiaE9+PsqCGt3u/tF8m9/efw8pGfWiXzNud2jA/ry4sjrsOg03OwIoc/7/yIEnOVvYT+ouNr6OffixdHXW8vWPdj7k4sHgQhQQYfnhx2lcdByM6y4zxz4BuHS2H1TZVuFVRo+r2goK6c27e+w6eT7iVSlI8XBOEMJgIRD+XUlvLU/q/ZUZbu9loVFe9Oqpb59AHH23rbo9Zq5uvsLaRVF3qcI+KMgsrx6kJ+nvEX1hcd5kRDNZGmQCZFpNh38PgZTFyVOIm8unIyaopdLgn938HvqLaamdVraKuKo9cmTSHRN5zF6evZeaLxZ9DbL5KrEidxQcxIZEkmr/YEd259l/qmQLHljpW06kLu3PoOn0y6F6POwLGqAmQNVV8vjRvL6NBkpkYMwOBmR5Ij76SuArQnwyqo1NrMfJa5kftSzvP4foIgCKeLLg9EzGYz48aNY8+ePezatYvhw4d39S27TGFdOQs3v6l5m6VNVZgWOVDztZuKj5JaVYBRZ2BKRAqxPqEA7DuRRbXV3O5+nyzWJ4TnD37rURCil3RuK4iWmKvw0RuZFT3M5XWHK3PdDvxlDdU8tf9rXj78Iy+Our5VldjJESlMjkjBolhRVLVNwujnmRsxK44PvLOpCtm1paws2M95MSPcbnNudl/KXEwaE1NPVmWpY3vZcY8/p6gqP+XtFoGIIAhntC7Phnv44YeJjj4z1rkXHV9LpaVOU3KnjMTEsH6a1vj3nsjkwjX/xwM7P+St1F/5z+EfuWTdizy2+xPqrA1sLUvtjO7b3bP9fQocnCjrTGN+RV+3ZeWDvXw1tad18IfGXS73bnufvNq2BdEMst7hrpVf8ve4/BlJSPxasBeAKREpLq+VkRgdktzuIARwu4TnSk0nBqCCIAinoi4NRH766SeWL1/OCy+80JW3+UNYFRvf5+50G4Q0l2gfE5rMk8Ovctvu8epC7tr2HiXmtmeQrC44wKO7P0Hq5HTi/LoTms5Hgcag4fkR13JVwkS3A/a82NGa2pwaMUDzqTMKKg2qlS+zNmv8hPuBX0WlytJY02N8WF/6+EU6DbIUVK5PmqL53o4Ee/niq/O8yJ0ExIjtvIIgnOG6bGmmsLCQW265hW+++QYfH23nfJjNZszm338DrKys7KrueazW1oC5KanQGQlI9A3n8SGXMjAwts2uj/TqIpZmb+N4dQG11gbCjf7k1JY1LjE4OYNkU8lRJob168xHsW8fdVfAa27MSO7rP4cgL18UVWF8WF+2lqS26atOkgnx8uWK+Ama7n9ezAjeTVtFRUOtpkJliqrya8E+7k2Zo6n9eN8wjlbmO1160kkyvZuKw8mSzH9H38g929/jeHWRw+ufPvANL4++gVifUHsl28L6csKNAcyLHc3ZUYNd5o0YZD0Xxo3hs8wNTrd6O3Np3DiPrhcEQTjddEkgoqoqN9xwA7fffjujR48mIyND0+eeeeYZ/vGPf3RFl+zqrA2sKtxPbm0Z/gZvZkQNJtIUiNlmYWtpGhWWWqK9gxgenNiqjoOPzguDrMOiOJ9JkCWZyREpDHJwXsi7qat4K/XXVrs9tFqSuYle3kEU1JV3WgVTaJy9kZu2i7YkIzEoKI6/Db7EPlMgSzLPj7iWFw9912ZmaFhQAk8MvUzzYYF+ehOvjVnI3dveo0xjqXl3QWBLl8WP5yknZ/ZA46zThXFj7F+HmwJ4evg1XLvhZYel2IvNldyx9R16eQezvyLb/jPMrille9lxPsvcyKujb8LPYHJ6z5uSp7Oh+DDZtaWaghEZicFB8a36KQiCcCbyqI7Io48+ynPPPefymkOHDrF8+XKWLFnC2rVr0el0ZGRkkJSU5DZZ1dGMSFxcXKfVEfk5bzfPHlhGrc2MXpLtA8LQoHiOVRVQY/v93tHewTw26CLGhfW1v/bkvq/4IW+XyyWKTyfdS7J/VKvXfsnbw9/3ft7ufks0LpEYJD01tvpOC0aCDL5EmAI4WpXfanZkWsRAHh9ymdOBtcxczfayNCyKjQGBMfT2i2zX/ettFn4t2MerR36irKHG6XUyMDq0D6+OuUlTu1bFxp92fMDW0jSHsyLzEye3SQB97sAyvsnZ5ia3xPEMkozEzKghbpfiKi11vJ36K9/m7LAvHw0KiKVfYDS/FR2muGl5zkfnxcVxY7mt78xWO4YEQRBOF11W0Ky4uJjSUtfnk/Tu3ZsrrriC7777rtXShM1mQ6fTMX/+fD744ANN9+vMgmbriw7z4M4PNV8vNWV7vDZmIaNCewON57dct/FVam3mNr/VSjQeWvf3IZe1el1VVa7Z8DLHq4s6tFVWRiLSO5C50SP5KW83VdY6Yn1CGRuazKLja9vV3vCQRN4YczMHKnI4UJGNXtIxLqyPfbdOZ6hoqMWiWAny8kUv69q8b1VszFr1FFVuzmH5vxHXcpbGHUgADYqVD46v5YvMTZRbaoHG3ULXJ53FhbGj2yybnbvyKcotzoMhdyQk3hhzM8n+kQR6uV6KNNsslJirMOkMhBr9gcZZmsymLc1xPmEdSo4VBEHobt1eWTUrK6tVfkdeXh6zZs3iyy+/ZNy4ccTGxrr49O86KxBRVZX5G17xuG6GjET/gGg+mHiX/bW0qkL+se9LDlfm2l/zkvVcET+BO/ud22awLTVXMWf1M+3u+8leH7OQ0U0n30JjXZNL1r3YrraeGnaVwwPzOsP6osO8n7aa/RXZQGMhsMvix3N977NaDbJrCg/y8K7Fbtv79qyHiWpHYS+rYqOgvhydJBNlCmoTgDSbtuL/UduB3S3NJCQmh/fnjn7n0uekmTFBEISeotsrq8bHx7f62s+vMXcgOTlZcxDSmXJqy0itLvD4cwoqhypzyawpIcE3DIBk/0g+nHgXhytySasuxKgzMC60D/4Gb4dtWF3klHhKQiKturBVIBLrE8rAwFgOVeRqDrIkYErEAGZEDW7T1/VFh0itLsSkMzA1YqD9uT3xZdZmnj/4baudMeWWWt5JW8W3Odt5c+wtxPo2zrrk1JZqypvZfSKDs42Ok0JzaktZkrmZNYUHsKg2BgXGckX8BMaG9UEv6zTN8CT7R3GgPLtdp/y2pKKyoeQoW0vTeGvcLQwM/OP/vguCIJxOesSpWlVWbQXInCk1V7V5LSUwhrkxI5kZNcRpEAIQavTXXF/DHRXVYc7Avf1nI0vNi0lttdw6Guzly619Z/Ls8GtabVndVprG3DXP8sjuT3gvbTWvHfmFy9f/m0d2fUytB7UsCusreOHgd039bavIXMlVG/7Lkco8APwN3poG/8f3LmHO6md469iKVufCbCk5xlW//ZcvsjZRUF9OqbmKDcVHuHv7e7x8+Ce0TvhdET+hw0FIM0VVsChWntz/teb7C4Ig9FR/SCCSmJiIqqrdVlW1l3cQsubKFW1FmNq/LKSXdVwRP8FpkOAJGYlJDrbyjgzpzUujFhB5Uj99dF7c138OK87+G19OeYAvpjzAD9MeZWHyjFZLSEcr87h/+yLKGxpzKWwtTsldW3iQx3Z/qnlA/fD4GrczMw2KlTu2vkNpfTVnRQxAr/GU2UpLHe+lreFPOz7AqtiotNTx0K7FWBRbqyTT5n9fnLGe1YUHNLV9Tq8hnBvVdpmq+e9NH79IZCfLOo4oqKRWFXC4KeASBEEQHOsRZ80Ee/kxNWIA64sOYfMwR2RwUFyHkzev6z2VXSfS2Vqa5vK6qeEDWFd8yOF7EhIXxo0hzElQND6sL9+c9RA7ytLJrS0j0ODNhPB+9hmUeBdLLO+lraaxjJrzWiYHKnIY7GBbcrMTDdX8fc8StpZqqwJbba3n/DXPcm/KHK5OnMxH6es0fU5FZVtpGt/n7qTWZsZss7g8zfeTjN/aLEE5vFaS+eewKxgeksinGRvIrm1Myh4UFMt1SWcxMiSJO7a+w7GTdhi5k1VTwgAPD8gTBEHoSbokWbWzdOaumdzaMm7Y9DrV1nrNJdp1ksyb425hSFA8x6ry+S5nJ4X15QR5+XJe9AiGBsU7TX48mVWxsSxnO59nbiSzpqTVoB/vE8bNfWYwq9cw3kr9lffTVjfu2mlq26YqnBM1hCeGXu5ReXRXffm1YB/fZG8jp7aUIrPrwnE6Seby+Ak8MGCuw/fNNgs3bHrd7UF2zvxt8CUcry7is4wN9u+Kq1kVCYm+/lHE+ISwpvCAy6BAQmLTrH+1qgnjjqqq1Noa0ElSq6WwepuFn/N2801O4/dNy5lD/x55PZMjUjTfWxAE4UzQ7btmOktnBiLQGIy8cuQn1hQetC89DAiIYURIEsvz91DSIhck2S+SRwZdyNCgeP7v4Ld8lb0VnSSjqIr9tNbJ4Sk8M/xqh+eduFNjNXOoIod1RYfYfyILi6owNDieS+PHYZK9+CFvJwV15QR6+TC713D6BfTq8PNDY9Dwpx0fsL3suObiajpJZk70cB4/aWtysx9yd/KPfV+2u0+RpkCWnfUQZeZqVhbsZ3H6egrNFS4/Y9IZmBjWn9WFB1wGLTISm2Y9iVW1UWKuwigbNBdec8VsszB79dMuz4Lx1Rn5acZfxFZcQRB6nG7fNXOqivEJ4dkR8ylvqKWovhw/vTfRTWd53N1vFntOZDZVVg2mf0A0kiTxXtpqvsreCvyee9D8z43FR/i/g9/ytyGXetyX1KoCHtjxIWbl96WF1OoCvszazJ8HXMAtfc7u+AM78MaxFewsSwfQnJypqioxPiFO3/8xbxcSUrvrpBTWV3C4Mo+BgbFcmTiRLaXHKCqudNmej87I6NDerC7c7/QaGYnhwYm8evQXlmZvpbqpVsnAwFgWJk9nSsSAdvUXwKgzsDB5Bi8f+cnpNTcmTxdBiCAIghs9YtfMyYK8fOgXEG0PQqAxqXRUaG9mRA0mJTAGSZIw2ywsTl/vtB0Fle9zd1JS79mZOGlVhdyx9W3qldb5DY0H3sH/HfrOHix0plqrma+ztrRrd8j5MaOotzWwuvAA3+ZsZ0fpcZSmgOyEuaZDxdqg9Smz5/Qa6rI9nSQxJ3o4c6JH4Kc3OU1EVlAprK/g4/T19iAE4HBFLg/u/Mijg/QcmZ84mZuTZ6CTZGQk9JIOGQkZiZuSp3NdBw/LEwRB6Al61IyIp/aXZ7cawBxRUNlcmsr5MSM1tZlbW8ZNm99weKZJM50k80nGb4wMSfKov+6kVhVQ78GZLc3u6Hsuv+bv453UlW3K4P9l8MXE+oSQXl3oUSLwyeJaJASfHTWE99JWk1Nb5vAcHJPsxRUJE/DVG/nP6Bu4d9v71Nka7AGWrmnpbExIMjvKjrcJvJq/fvHQ90yLHERYU3VTT0mSxK19Z3JJ/LjGpb36KkKN/syKHtbuNgVBEHqaHjkjolXLehUur7NpH9yfO7DM7TH1NlVha4m23See8WwLcZxPKP8YejlI8N8jP7YKQgDy68q5b/siBgfFtTsI0Uky40P7tqqa6iXreW3MQvo2VSbVSbJ9i2+o0Z/Xxi6kl3fjbNaQoHi+mvogt/U9h4GBsST7RXJ+9EgWTbiT1OoCl7M/qqryQ+7OdvW7pTCjP9ckTubelDnMT5osghBBEAQPiBkRF5L9IzUldPYPiNbUXl7tCTaXHtN0bWcV12qpX0AU3jovt4HQU8Ouop9/L+J9w6iy1jNn9dMOr1NRUdXGMu3N26NP7rWMRIJvONm1JSiq2uq5dJKMr97Inwde0KbtCFMgH0y4iz3lmWwuOYZNURgUFMfk8P5tyuiHGP24MXkaNyZPs79WbannhIuD9KBxRiO9usjlNYIgCELXEoGICxGmwMYBtviww22psiSR7BepuYx3eo22QU8ChgYleNJVTUw6Ly6LH8/i9PUOczB0kszQoPhW58+sLjzgsky9gsr+imyWTP4Tvf0iWJK5yX5mi1HWMy92NHf3m01adSFvHVvBlqY6IzpJ5uyowdzR91ynibCS1JhsOjw40eNn9dLpNSXQ+ujF6baCIAjdSQQiTWyqwv7ybMobaoj2Caavf+N22YcHzuPI5jwK6yva/Dbvo/Pin0Ov1FxLxFvjDgoVuDpxksfP4MjBpi3CZpuFfgG9uKH3WaRVFbCx5Kh9tqe5QFe0dzBPDmt9lH2Zudq+XdmVv+z+lJEhSbw59mbMig1FVegb0As/vQmAwUFxvDLmJk40VFPZUEeo0R8/g6lTntERL1nP5PD+bCw56rTvNlXh7MghXdYHQRAEwT0RiAA/5+3m1SM/tyrs1c+/Fw8PvJChwfF8MPEuPsnYwNLsrVRYavHWeXF+zEiuTZpiz1XQYkhQPAEGb7eFsK5KmMSUDhbBqmio5dHdn7Cj7Dg6qXFfiVVV8NebmNVrOGFe/pQ0NNZNMem8mN1rGPenzMX7pBmCcFOApiJlqdUFpNcUsSRrE/f1n8N8JztGgr38CPbqeB0PLW5Mns6G4iMO35OaquZ2dkKwIAiC4JkeVdDMkW9ztvPk/q/bvN5cWfWtcbe2Km1uVWyNA7sH54609HH6ev7rovbEtIiBPD/y2na13UxVVW7e8iYHK3I1V5FVULm1z9ncfFL9khqrmTmrnvZ4t81LoxYwKby/w/dKzVX8mLeLrJoSfHRGZvYawuDAuHZ/T50prK/g6t/+63Tn08LkGdzWd2an3lMQBEHwbPzu0btm6m0NvHToB4fvKajYVIX/HP6x1et6WdehAfOaxMksSDoLicYcE50k20/BnRszkqeHX93utpttK0tjX3m25nLrzUtO/0tdydGTDmnz1Ru5N2WOR/eXkfjw+FqH732ZtZnz1zzHq0d+4fvcnSzJ2sTCzW9y9/b33G6V9tQ7x1a6TMxddHxNq2q6giAIwh+vRwci64oOtdmS2pKCyt7yTHKaDkDrDJIkcVf/WXw99c8sTJ7B3JiRXJc0hc8m38cTQy5rsyOkPVYW7LcHN57QSTJfN1WRbemy+PH8ffClmrelKqjsOpGB5aTtz2sKD/L8wW+bCrc1BnrNwdKO0uP8bfdnHvfZmXqbhR/zd7kMxlRV5ae8XZ12T0EQBMFzPTpHpLi+UtP23KL6ig6fwHuyGJ+QLivjXms1054VN5uqOD22/oLYUcyJHs7e8izeOraCPScy3X7flJP68E7qSqc7WRRUNpYc5VhVvj1RuCMqGmqwuNjtA40zUvl1Jzp8L0EQBKH9evSMSIjRT1O9jpA/KLmys8T7hrX7s4qLGQS9rGNkSBIzoga7OR0XevtFtDoMsLC+gqNV+W4PqFtTeLBd/T6Zn8EbyU0BNxUIMvh2yv0EQRCE9unRgcjUiIEYZedbaiUk+vtHk+gX8Qf2quPmxYxq92ePVuVzoDzb5TXnRY/ApPNyOsw3bj+e3Oq1eg3VZxVUsms6ZxnMV29kcnh/p+fQQOMM0KxewzrlfoIgCEL79OhAxFdv5Pa+5zh8T2r6c0/K7D+0T50h0jvInmDqblbgZKqqutzVA+Bv8ObZ4degl3StclGaB/250SO44KSzdyJNgZhcBH3NVhXu77QE0lv6nI1Olh1+DyQk5kaPIMEvvFPuJQiCILRPjw5EAK5JnMQDKXPx1RtbvR5hCuTFUdczNrRPN/WsY65JnMwzw68m2T/S/pqvzsggN1VgVWD3iQxya8tQVZVtpWk8vmcJt275H4/t/oTfihqrzE4I78fiSfcwL3Y0QQZffHRGhgTF89Swq3h8yGXIJyXLmnQGLoh1P1NjVWx84yBhtj1SAmN4ZfRNRJgat441B0oyEpfEjeWvgy/plPsIgiAI7dfj64g0q7dZ2FxyzF5ZdXRI7zaD6elIVVWKzZU0KFYiTIF8lrGR14/+4jY35pqESWwrTeNYdYH9NFsdEjZURoYk8e+R1+NzUvDmTqWljtmrnnJ58jA0FpNbPOkej9p2pfkQwfSaInx0RiZHpIiD6QRBELqQJ+N3j94105JJZ2Ba5MDu7kankySJCFOg/WutCbqfZG6w/3vzFtjmE3Z3l2Xw7IFv+OewKz3qS4DBm2AvP4pbVLB1ROupx1rpJJkJ4f2YEN6vU9sVBEEQOu70/5Vf8Mi0iIEY5Y7Fnwoqy/P3UlzvOqBwZFBgrMsaJzpJ1nyIoCAIgnD6E4FID+NnMHFrJ5Q1V2jMH/HU5QkTXBYZs6kKl8eP70jXBEEQhNOICEROU2Xmalbk7+XH3F1kVBd59NlrE6c4TND1lNYS8i2NCU3muqSpAK221jb/+219ZjKoxdk+giAIwplN5IicZuptFl489B3f5+5sFQiMDEni/w25nCjvILdtSJLEVYmTuChuLF9kbeaj42spt9R63Jf2LqHc3W8WgwJj+TjjN/aXZwESw4ITuDZpClMiBrSrTUEQBOH0JAKR04iiKjy8azFbS1LbJJzuOZHJzZvfYvGkuwny0lYttKi+gndSV2LWUGysJZ0kMyQovtXWYE9IksSMqMHMiBpsr+R6JuxQEgRBEDwn/u9/GtleepzNJccc7nqxqQol5kqWZG7S3N7i9PU0KFZNu2iayUiEePnxj6GXa/6My/YkWQQhgiAIPZgYAU4jP+TtdLnjREHl25ztmtpqPHl2t+Y8DwkINwawsM8MPp50D728gzV9ThAEQRBcEUszp5Hi+kq3gcOJhhpNbVlVG2bF/ZKMBMyJHsEDA84nwOCtqW1BEARB0EoEIqeRSFOQvcqpM6EaK4YaZD0hXn6UNVQ7vaaxFPo4Hh40z+O+CoIgCIIWYmnmNHJ+zEiXQYiMxIVxozW3d0ncWJen0yqoXJEganoIgiAIXUcEIqeRkSFJTIsY6PA0WZ0k08s7mMvjJ2hu7+rEScT5hjrNO7k2aQqJfhHt7q8gCIIguCMCkdOIJEk8NfwqrkqYiFeLMu0SEhPD+vH2+Ns8yuPwN3jz9rjbmRM9HH2LYCTM6M+DA87nnn6zO7X/giAIgnAycfruaaraUs/uExlYVYWUgGhNhcxcqbTUkVFdjFGnJ9kvEr2s65yOCoIgCD2OOH23B/AzmJgckdJp7QUYvBkaHN9p7QmCIAiCFmJpRhAEQRCEbiMCEUEQBEEQuo0IRARBEARB6DYiEBEEQRAEoduIQEQQBEEQhG4jAhFBEARBELqNCEQEQRAEQeg2IhARBEEQBKHbiEBEEARBEIRuc0pXVm2uPl9ZWdnNPREEQRAEQavmcVvLKTKndCBSVVUFQFxcXDf3RBAEQRAET1VVVREYGOjymlP60DtFUcjLy8Pf3x9Jkrq1L5WVlcTFxZGdnX1GHsB3pj8fiGc8U4hnPP2d6c8H4hlVVaWqqoro6Ghk2XUWyCk9IyLLMrGxsd3djVYCAgLO2L9UcOY/H4hnPFOIZzz9nenPBz37Gd3NhDQTyaqCIAiCIHQbEYgIgiAIgtBtRCCikdFo5IknnsBoNHZ3V7rEmf58IJ7xTCGe8fR3pj8fiGf0xCmdrCoIgiAIwplNzIgIgiAIgtBtRCAiCIIgCEK3EYGIIAiCIAjdRgQigiAIgiB0GxGICIIgCILQbUQg0gFms5nhw4cjSRK7d+/u7u50qnnz5hEfH4/JZKJXr15cd9115OXldXe3Ok1GRgYLFy4kKSkJb29vkpOTeeKJJ2hoaOjurnWap556iokTJ+Lj40NQUFB3d6dTvPbaayQmJmIymRg3bhxbt27t7i51qnXr1nHBBRcQHR2NJEl888033d2lTvXMM88wZswY/P39iYiI4KKLLuLIkSPd3a1O9cYbbzB06FB7tdEJEybw008/dXe3usyzzz6LJEncf//97W5DBCId8PDDDxMdHd3d3egS06dPZ8mSJRw5coSvvvqKtLQ0Lrvssu7uVqc5fPgwiqLw1ltvceDAAV566SXefPNN/vKXv3R31zpNQ0MDl19+OXfccUd3d6VTfP755zzwwAM88cQT7Ny5k2HDhjFr1iyKioq6u2udpqamhmHDhvHaa691d1e6xNq1a7nrrrvYvHkzK1aswGKxcO6551JTU9PdXes0sbGxPPvss+zYsYPt27czY8YMLrzwQg4cONDdXet027Zt46233mLo0KEda0gV2uXHH39UU1JS1AMHDqiAumvXru7uUpdatmyZKkmS2tDQ0N1d6TLPP/+8mpSU1N3d6HTvv/++GhgY2N3d6LCxY8eqd911l/1rm82mRkdHq88880w39qrrAOrSpUu7uxtdqqioSAXUtWvXdndXulRwcLD6zjvvdHc3OlVVVZXat29fdcWKFepZZ52l3nfffe1uS8yItENhYSG33HILH330ET4+Pt3dnS5XVlbGxx9/zMSJEzEYDN3dnS5TUVFBSEhId3dDcKChoYEdO3Ywc+ZM+2uyLDNz5kw2bdrUjT0TOqKiogLgjP3vzmaz8dlnn1FTU8OECRO6uzud6q677mLu3Lmt/ptsLxGIeEhVVW644QZuv/12Ro8e3d3d6VKPPPIIvr6+hIaGkpWVxbJly7q7S10mNTWVV155hdtuu627uyI4UFJSgs1mIzIystXrkZGRFBQUdFOvhI5QFIX777+fSZMmMXjw4O7uTqfat28ffn5+GI1Gbr/9dpYuXcrAgQO7u1ud5rPPPmPnzp0888wzndKeCESaPProo0iS5PLP4cOHeeWVV6iqquKxxx7r7i57TOszNnvooYfYtWsXy5cvR6fTcf3116Oe4icCePqMALm5ucyePZvLL7+cW265pZt6rk17nk8QTkV33XUX+/fv57PPPuvurnS6/v37s3v3brZs2cIdd9zBggULOHjwYHd3q1NkZ2dz33338fHHH2MymTqlTXHWTJPi4mJKS0tdXtO7d2+uuOIKvvvuOyRJsr9us9nQ6XTMnz+fDz74oKu72m5an9HLy6vN6zk5OcTFxbFx48ZTeorR02fMy8tj2rRpjB8/nkWLFiHLp3Zs3p6f4aJFi7j//vspLy/v4t51nYaGBnx8fPjyyy+56KKL7K8vWLCA8vLyM3K2TpIkli5d2up5zxR33303y5YtY926dSQlJXV3d7rczJkzSU5O5q233ururnTYN998w8UXX4xOp7O/ZrPZkCQJWZYxm82t3tNC39mdPF2Fh4cTHh7u9rqXX36ZJ5980v51Xl4es2bN4vPPP2fcuHFd2cUO0/qMjiiKAjRuWT6VefKMubm5TJ8+nVGjRvH++++f8kEIdOxneDrz8vJi1KhRrFy50j4wK4rCypUrufvuu7u3c4Jmqqpyzz33sHTpUtasWdMjghBo/Lt6qv+/U6uzzz6bffv2tXrtxhtvJCUlhUceecTjIAREIOKx+Pj4Vl/7+fkBkJycTGxsbHd0qdNt2bKFbdu2MXnyZIKDg0lLS+Pvf/87ycnJp/RsiCdyc3OZNm0aCQkJvPDCCxQXF9vfi4qK6saedZ6srCzKysrIysrCZrPZa9306dPH/vf2dPLAAw+wYMECRo8ezdixY/nPf/5DTU0NN954Y3d3rdNUV1eTmppq/zo9PZ3du3cTEhLS5v89p6O77rqLTz75hGXLluHv72/P7wkMDMTb27ube9c5HnvsMebMmUN8fDxVVVV88sknrFmzhl9++aW7u9Yp/P392+T0NOcStjvXp1P28fRg6enpZ9z23b1796rTp09XQ0JCVKPRqCYmJqq33367mpOT091d6zTvv/++Cjj8c6ZYsGCBw+dbvXp1d3et3V555RU1Pj5e9fLyUseOHatu3ry5u7vUqVavXu3wZ7ZgwYLu7lqncPbf3Pvvv9/dXes0N910k5qQkKB6eXmp4eHh6tlnn60uX768u7vVpTq6fVfkiAiCIAiC0G1O/UVxQRAEQRDOWCIQEQRBEASh24hARBAEQRCEbiMCEUEQBEEQuo0IRARBEARB6DYiEBEEQRAEoduIQEQQBEEQhG4jAhFBEARBELqNCEQEQRAEQeg2IhARBEEQBKHbiEBEEARBEIRu8/8BKhpOqoJblqsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plot\n",
    "classes, points = generate_data(1000)\n",
    "plot.scatter(x=points[:,0], y=points[:,1], c=classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rskLHEI9qSUg"
   },
   "source": [
    "Now let's create the model for our CGAN.  DeepChem's GAN class makes this very easy.  We just subclass it and implement a few methods.  The two most important are:\n",
    "\n",
    "- `create_generator()` constructs a model implementing the generator.  The model takes as input a batch of random noise plus any condition variables (in our case, the one-hot encoded class of each sample).  Its output is a synthetic sample that is supposed to resemble the training data.\n",
    "\n",
    "- `create_discriminator()` constructs a model implementing the discriminator.  The model takes as input the samples to evaluate (which might be either real training data or synthetic samples created by the generator) and the condition variables.  Its output is a single number for each sample, which will be interpreted as the probability that the sample is real training data.\n",
    "\n",
    "In this case, we use very simple models.  They just concatenate the inputs together and pass them through a few dense layers.  Notice that the final layer of the discriminator uses a sigmoid activation.  This ensures it produces an output between 0 and 1 that can be interpreted as a probability.\n",
    "\n",
    "We also need to implement a few methods that define the shapes of the various inputs.  We specify that the random noise provided to the generator should consist of ten numbers for each sample; that each data sample consists of two numbers (the X and Y coordinates of a point in 2D); and that the conditional input consists of `n_classes` numbers for each sample (the one-hot encoded class index)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q5s_qNouqSUk"
   },
   "outputs": [],
   "source": [
    "# Creating the Generator\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self, noise_input_shape, conditional_input_shape):\n",
    "        super(Generator, self).__init__()\n",
    "        self.noise_input_shape = noise_input_shape\n",
    "        self.conditional_input_shape = conditional_input_shape\n",
    "        self.noise_input_dim = noise_input_shape[1:]\n",
    "        self.conditional_input_dim = conditional_input_shape[1:]\n",
    "\n",
    "        input_dim = sum(self.noise_input_dim) + sum(self.conditional_input_dim)\n",
    "\n",
    "        self.output = nn.Sequential(\n",
    "            nn.Linear(in_features=input_dim, out_features=30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=30, out_features=30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=30, out_features=2)\n",
    "        )\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        noise_input, conditional_input = inputs\n",
    "        inputs = torch.cat((noise_input, conditional_input), dim=1)\n",
    "        return self.output(inputs)\n",
    "\n",
    "\n",
    "# Creating the Discriminator\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self, data_input_shape, conditional_input_shape):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.data_input_shape = data_input_shape\n",
    "        self.data_input_dim = data_input_shape[1:]\n",
    "        self.conditional_input_shape = conditional_input_shape\n",
    "        self.conditional_input_dim = conditional_input_shape[1:]\n",
    "\n",
    "        input_dim = sum(self.data_input_dim) + sum(self.conditional_input_dim)\n",
    "\n",
    "        self.output = nn.Sequential(\n",
    "            nn.Linear(in_features=input_dim, out_features=30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=30, out_features=30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=30, out_features=1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        data_input, conditional_input = inputs\n",
    "        inputs = torch.cat((data_input, conditional_input), dim=1)\n",
    "        return self.output(inputs)\n",
    "\n",
    "class ExampleGAN(dc.models.torch_models.GANModel):\n",
    "    def get_noise_input_shape(self):\n",
    "       return (batch_size,10,)\n",
    "\n",
    "    def get_data_input_shapes(self):\n",
    "       return [(batch_size,2,)]\n",
    "\n",
    "    def get_conditional_input_shapes(self):\n",
    "      return [(batch_size,n_classes,)]\n",
    "\n",
    "    def create_generator(self):\n",
    "      noise_dim = self.get_noise_input_shape()\n",
    "      conditional_dim = self.get_conditional_input_shapes()[0]\n",
    "      return nn.Sequential(Generator(noise_dim,conditional_dim))\n",
    "\n",
    "    def create_discriminator(self):\n",
    "      data_dim = self.get_data_input_shapes()[0]\n",
    "      conditional_dim = self.get_conditional_input_shapes()[0]\n",
    "      return nn.Sequential(Discriminator(data_dim,conditional_dim))\n",
    "\n",
    "\n",
    "gan = ExampleGAN(learning_rate=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lnd0Wk9WqSU_"
   },
   "source": [
    "Now to fit the model.  We do this by calling `fit_gan()`.  The argument is an iterator that produces batches of training data.  More specifically, it needs to produce dicts that map all data inputs and conditional inputs to the values to use for them.  In our case we can easily create as much random data as we need, so we define a generator that calls the `generate_data()` function defined above for each new batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3o85U5VJqSVG",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ending global_step 999: generator average loss 0.798101, discriminator average loss 1.19575\n",
      "Ending global_step 1999: generator average loss 0.979523, discriminator average loss 1.01209\n",
      "Ending global_step 2999: generator average loss 1.02887, discriminator average loss 1.10792\n",
      "Ending global_step 3999: generator average loss 0.97388, discriminator average loss 1.14065\n",
      "Ending global_step 4999: generator average loss 0.738954, discriminator average loss 1.3596\n",
      "Ending global_step 5999: generator average loss 0.699314, discriminator average loss 1.39426\n",
      "Ending global_step 6999: generator average loss 0.700374, discriminator average loss 1.38509\n",
      "Ending global_step 7999: generator average loss 0.702294, discriminator average loss 1.38039\n",
      "TIMING: model fitting took 102.707 s\n"
     ]
    }
   ],
   "source": [
    "def iterbatches(batches):\n",
    "  for i in range(batches):\n",
    "    classes, points = generate_data(gan.batch_size)\n",
    "    classes = dc.metrics.to_one_hot(classes, n_classes)\n",
    "    yield {gan.data_inputs[0]: points, gan.conditional_inputs[0]: classes}\n",
    "\n",
    "gan.fit_gan(iterbatches(8000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m91nmqWgqSV1"
   },
   "source": [
    "Have the trained model generate some data, and see how well it matches the training distribution we plotted before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JqJCBFIcqSV3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe7d83d9d20>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes, points = generate_data(1000)\n",
    "one_hot_classes = dc.metrics.to_one_hot(classes, n_classes)\n",
    "gen_points = gan.predict_gan_generator(conditional_inputs=[one_hot_classes])\n",
    "plot.scatter(x=gen_points[:,0], y=gen_points[:,1], c=classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "StyDTNfRqSV8"
   },
   "source": [
    "# Congratulations! Time to join the Community!\n",
    "\n",
    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
    "\n",
    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
    "\n",
    "## Join the DeepChem Discord\n",
    "The DeepChem [Discord](https://discord.gg/cGzwCdrUqS) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "16_Conditional_Generative_Adversarial_Networks.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tempenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
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